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Author SHA1 Message Date
Patrick Plate c25a97c37b docs(cannamanage): add complete Phase 0 documentation suite
- 01-PROJECT-CHARTER.md: project charter with Gantt chart and risk register
- 02-USER-STORIES.md: 25 user stories with MoSCoW priorities and ACs
- 03-ARCHITECTURE.md: system architecture, ERD (8 entities), multi-tenancy design
- 04-FLOWCHARTS.md: 5 business logic flow charts (distribution, recall, etc)
- 05-API-SPEC.md: REST API spec (7 controllers, 30+ endpoints)
- 06-WIREFRAMES.md: 6 screen wireframes with AI-generated mockup images
- 07-CODING-STANDARDS.md: Java 21 standards, Git strategy, compliance rules
- 08-TEST-PLAN.md: 26 test cases, JaCoCo coverage gates
- 09-DEPLOYMENT-GUIDE.md: Hetzner Docker Compose + Gitea CI/CD pipeline
- README.md + CHANGELOG.md + 10-RETROSPECTIVE.md
- 5 AI-generated UI mockup images (Flux Schnell/ComfyUI)
2026-04-06 11:07:35 +02:00
Patrick Plate a72a2efceb feat(mcp-image-gen): merge ComfyUI auto-start health check + systemd service 2026-04-06 10:43:44 +02:00
Patrick Plate c662a5237b feat(mcp-image-gen): add ComfyUI auto-start health check + systemd service
Option A: Add lifespan context manager to server.py
- _ping_comfyui(): async health check against /system_stats
- check_and_start_comfyui(): ping on startup; if down, launches ComfyUI
  via subprocess.Popen from COMFYUI_DIR (.venv/bin/python main.py)
  with HSA_OVERRIDE_GFX_VERSION=11.0.0 injected for AMD ROCm
- Polls up to 30s for readiness after auto-start
- New env var: COMFYUI_DIR (default ~/ComfyUI)
- FastMCP lifespan= wired in; 34/34 tests still passing

Option B: Add comfyui.service systemd user service file
- Install: cp mcp/mcp-image-gen/comfyui.service ~/.config/systemd/user/
- Enable: systemctl --user enable --now comfyui
- Sets HSA_OVERRIDE_GFX_VERSION=11.0.0, WorkingDirectory=%h/ComfyUI
- Restart=on-failure, logs via journald

docs: Update mcp-image-gen-ComfyUI-Setup.md
- New Step 4: systemd service install + linger instructions
- Step 5: manual start (moved from old Step 4)
- Step 6/7 renumbered; COMFYUI_DIR env var documented
- Architecture diagram added; troubleshooting rows updated
2026-04-06 10:43:36 +02:00
Patrick Plate 0ff3f20589 feat(mcp-image-gen): merge name and count params into main 2026-04-06 07:45:45 +02:00
Patrick Plate 79f1e6d65f feat(mcp-image-gen): add name and count params to generate_image
- Add name (str) param: filename prefix saved as {name}_{timestamp}_{seed}.png
- Add count (int, 1-10) param: generate N images in one call
- Extract _sanitize_name() helper: strips special chars, collapses underscores, caps at 64 chars
- Extract _build_filename() helper: pure function for testable filename construction
- Extract _generate_single() coroutine: clean loop body for batch generation
- Fixed seed batches increment seed per image (seed+i-1) for deterministic variation
- random seed (-1) batches give independent random seeds per image
- Partial batch failures continue (error TextContent in slot, remaining images proceed)
- Returns flat interleaved [Text1, Image1, Text2, Image2, ...] list
- 34/34 tests passing (was 19, added 15 new tests)
2026-04-06 07:45:37 +02:00
Patrick Plate 79a2e1d10a Merge branch 'feat/roo/ollama-backed-modes' 2026-04-05 10:27:37 +02:00
Patrick Plate 78de59243c feat(roo): add Ollama-backed doc-writer and ask-lite modes 2026-04-05 10:27:26 +02:00
Patrick Plate db8505fef1 merge: docs/wiki/promote-webscraper-search-hint → main 2026-04-05 10:11:37 +02:00
Patrick Plate 4107b8ede2 docs: promote webscraper_search_hint in wiki and mode rules 2026-04-05 10:11:33 +02:00
Patrick Plate 4202094f01 merge: fix/webscraper/search-hint-quality → main 2026-04-05 09:57:47 +02:00
Patrick Plate 62c3b67e66 fix(mcp-webscraper): improve search_hint quality — quote_plus, richer hint, dedup, result_count
- Use urllib.parse.quote_plus instead of str.replace(' ', '+') for correct
  URL encoding of special chars (&, %, +, #, =)
- Add search_url field to return dict so caller can verify/debug the query
- Add result_count field for quick summary without len(results)
- Deduplicate results by URL via seen_urls set
- Filter cards with both empty title AND empty snippet
- Richer hint string: 'Title (url): snippet[:120]' pipe-separated
- Max-results guard now breaks early (no over-fetching)
- 5 new tests (23→28): URL encoding, result_count, dedup, empty filter, hint format
2026-04-05 09:57:43 +02:00
Patrick Plate c2dd262727 chore(roo): document git-based wiki workflow in rules, skill, and README 2026-04-05 09:53:08 +02:00
Patrick Plate 9c2422d0a7 chore(roo): document git-based wiki workflow in rules, skill, and README
- mcp-builder rules: add wiki/ to structure diagram, add Wiki Update
  Workflow section (MANDATORY), update After Building a Server checklist
- gitea-push skill: add wiki deploy as a valid use case
- README.md: add wiki section with deploy_wiki.sh pointer, add
  mcp-image-gen to MCP servers table
2026-04-05 09:53:05 +02:00
Patrick Plate 9a8403ad57 docs(wiki): migrate to git-based workflow with persistent wiki/ clone 2026-04-05 09:48:22 +02:00
Patrick Plate dabdda167f docs(wiki): migrate to git-based workflow with persistent wiki/ clone
- Extract all wiki content from create_wiki_pages.py into docs/wiki/pages/*.md
- Add docs/wiki/deploy_wiki.sh: copies pages to wiki/ repo, commits, pushes
- Add /wiki/ to .gitignore (anchored — does not affect docs/wiki/)
- 12 pages: Home, MCP-Servers-Overview, mcp-image-gen, ComfyUI-Setup,
  mcp-webscraper (8 tools incl. search_hint), BigMind (schema v8),
  Development-Conventions, Java-Projects, Java-wellmann-shop,
  Java-mss-failsafe, Java-Architecture, _Sidebar
- Workflow: edit docs/wiki/pages/*.md → ./docs/wiki/deploy_wiki.sh
2026-04-05 09:48:19 +02:00
Patrick Plate da90781cad Merge feat/webscraper/brave-search-hint into main 2026-04-05 09:37:38 +02:00
Patrick Plate 2ab847f51d feat(webscraper): add Brave Search hint tool and User-Agent header
- Add webscraper_search_hint() tool using Brave Search as backend
  (no CAPTCHA/GDPR consent wall, works with plain httpx)
- Add User-Agent header to _fetch_page() — fixes 403 on Wikipedia,
  Feynman Lectures, and other sites that block headless requests
- Add 5 new tests for search hint (23 total, 90% coverage)

Brave Search URL: https://search.brave.com/search?q={query}&source=web
Use sparingly — once per research task as orientation, not in loops
2026-04-05 09:37:30 +02:00
Patrick Plate d5510f590e Added new picture for bigmind page 2026-04-04 20:03:59 +02:00
Patrick Plate cf102e8b3e fix(bigmind): render achievement card background images via inline style 2026-04-04 19:29:15 +02:00
Patrick Plate 13659fd414 fix(bigmind): add background-image inline style to achievement card ach-image divs
The .ach-image div had correct CSS dimensions (64x64) and background-size:cover
but was missing the inline style="background-image: url(...)" — so the div
rendered as an empty circle. Fixed by extracting img_url variable and applying
it as style attribute in the f-string. All 39 achievement PNGs now load.

303/303 tests passing.
2026-04-04 19:27:24 +02:00
pplate c68acdd030 chore(bigmind): rename timestamp badge PNGs to achievement IDs
- Renamed 19 timestamp-named PNGs (20260404_*) to match original
  achievement IDs in profile_builder.py compute_achievements() order:
  first_breath, first_thought, eureka, honest_mind, scholar,
  deep_knowledge, scientist, veteran, on_fire, storyteller,
  night_owl, speed_thinker, first_handshake, birthday, shared_mind,
  frugal_mind, quarter_million, token_millionaire, sniper
- Deleted 2 duplicate/excess timestamp PNGs
- Added image= field to all 19 original _add() calls in
  profile_builder.py so every achievement now has a PNG path
- All 39 achievements (19 original + 20 tiered) now have image fields
- 303/303 tests pass
2026-04-04 19:09:01 +02:00
pplate e61c9c98f5 fix(bigmind): fix static image path, JS string concat in achievements; add networker badge PNGs 2026-04-04 19:01:56 +02:00
Patrick Plate 50488109aa Merge branch 'feat/bigmind/achievements-rework' 2026-04-04 18:50:55 +02:00
pplate dd244a8e6c feat(bigmind): add tiered AI-generated achievement badges with image rendering 2026-04-04 18:50:45 +02:00
Patrick Plate ee07dec4d3 Merge feat/bigmind/profile-image-gallery into main 2026-04-04 14:52:36 +02:00
Patrick Plate 67b8b44408 feat(bigmind): profile image + AI image gallery (schema v8)
- web.py: add /profile-image route (serves most-recent gallery PNG)
  add /gallery/image/<filename> route (per-image serving)
  add /gallery route (renders gallery page from DB)
  add _get_profile_image_path() helper
- web_render.py: replace emoji avatar with <img src=/profile-image>
  onerror fallback to 🧠 emoji
  add .nav bar with Profile/Gallery links to both pages
  add _render_gallery_html() full gallery page renderer
  add gallery CSS: .gal-grid, .gal-card, .gal-img, .gal-info, etc.
- db.py: bump SCHEMA_VERSION 7→8
  add gallery_images table (id, filename, prompt, tags, model,
    created_at, width, height, file_size_bytes)
  add _migrate_v7_to_v8() migration function
  add init_db() hook for v<8 migration
- tests: update test_schema_version_is_7→8 in test_db.py and
  test_feature7_live_sessions.py; add gallery_images to expected tables

Storage strategy: Option B (filesystem + DB metadata)
Images in ~/.mcp/bigmind/gallery/, metadata in SQLite
Pre-populated with 5 lumen_profiles images (seeds 2409122067,
764633840, 1367851518, 3135233944, 568659042)

Tests: 297/297 passing
2026-04-04 14:52:30 +02:00
Patrick Plate a852e2ec0d docs: merge Java wiki header images 2026-04-04 14:40:57 +02:00
Patrick Plate a275a18e58 docs: add Java project wiki header images 2026-04-04 14:40:50 +02:00
Patrick Plate 20228f8d46 docs: add wiki creation script 2026-04-04 14:33:31 +02:00
Patrick Plate 3b1d5bf35c docs: add wiki header images generated by mcp-image-gen 2026-04-04 14:22:29 +02:00
Patrick Plate e12479a63a Merge branch 'feat/mcp-image-gen/tests-and-lumen-profiles' 2026-04-04 14:09:19 +02:00
Patrick Plate 64c0a62b49 feat(mcp-image-gen): add test suite (19 tests) and Lumen profile pictures 2026-04-04 14:09:11 +02:00
Patrick Plate f24aafec69 fix(mcp-image-gen): merge HF authenticated download fix 2026-04-04 12:28:28 +02:00
pplate 4165018ab2 fix(mcp-image-gen): fix HuggingFace authenticated download instructions
FLUX.1-schnell is a gated model — bare wget returns HTTP 401.

- Replace bare wget with huggingface-cli login + download (Option A)
- Add wget with Authorization header as Option B
- Add license acceptance prerequisite (huggingface.co gated repo)
- Add token creation link (huggingface.co/settings/tokens)
- Add fp8 quantized variant as alternative (~8.1GB, faster inference)
- Add download size note (~8GB, 10-30min)
2026-04-04 12:28:20 +02:00
pplate 2f01ff0639 fix(mcp-image-gen): correct ComfyUI install instructions in USAGE.md
ComfyUI is NOT on PyPI — `pip install comfyui` fails with
"No matching distribution found". Remove the wrong Option A.

Replace with:
- Warning note that pip install does not work
- Only correct method: git clone from GitHub + pip install -r requirements.txt

ROCm status confirmed: rocm-smi 3.1.0 / ROCm-SMI-LIB 7.7.0 installed.
2026-04-04 12:20:28 +02:00
Patrick Plate 7a21b02081 Merge branch 'feat/mcp-tool-limit' 2026-04-04 12:16:15 +02:00
pplate 1340d3098f fix(mcp): finalize alwaysAllow restrictions in mcp.json 2026-04-04 12:16:14 +02:00
pplate 8cbeb6571b docs(mcp-image-gen): add USAGE.md and expand tests to 19 2026-04-04 12:16:03 +02:00
pplate b0ce5c55ed fix(mcp): further restrict alwaysAllow in mcp.json after merge 2026-04-04 12:15:58 +02:00
pplate ef960a4b59 feat(mcp): limit tools to fix overload (#1)
Restrict alwaysAllow in .roo/mcp.json to essential tools per server:
- git: 5 tools (status, diff, log, add, commit) — was wildcard *
- gitea: 8 tools (create/list/get/edit issues, PR, repo) — was wildcard *
- playwright: 6 tools (navigate, click, fill, screenshot, close, new_context) — was unrestricted

Reduces total registered tools from 105+ to ~40, eliminating context
bloat and VS Code/Roo registration failures.

Closes #1
2026-04-04 12:03:07 +02:00
Patrick Plate 93b250c7a1 Merge branch 'chore/roo/mcp-config-update' 2026-04-04 11:54:33 +02:00
Patrick Plate 0a58541f1e chore(roo): update mcp.json config 2026-04-04 11:54:26 +02:00
Patrick Plate b30919cabb Merge branch 'feat/mcp-image-gen/comfyui-image-generation-server' 2026-04-04 11:49:44 +02:00
Patrick Plate 8112ff2f12 feat(mcp-image-gen): scaffold ComfyUI-backed image generation MCP server
- FastMCP server with 4 tools: generate_image, list_available_models,
  get_generation_status, get_output_directory
- ComfyUI REST API client (httpx) polling lifecycle
- FLUX.1-schnell workflow JSON template
- Dual output: TextContent (path + seed) + ImageContent (base64 PNG)
- 14 passing pytest tests with respx HTTP mocking
- ROCm/AMD RX 7900 XTX optimized setup in README
- Ollama Linux migration path documented (future)
2026-04-04 11:49:31 +02:00
Patrick Plate ba7d4bc248 feat(roo): merge gitea-playwright-mcp into main 2026-04-04 11:14:53 +02:00
Patrick Plate 29d6463f7c feat(roo): add forgejo-mcp + playwright MCP to .roo/mcp.json
- forgejo-mcp v0.0.7 binary installed at ~/.local/bin/forgejo-mcp
  (downloaded from github.com/raohwork/forgejo-mcp releases)
  Enables: Issues, labels, milestones, wiki, PRs, releases via Gitea REST API
- @playwright/mcp added for browser automation (replaces archived puppeteer MCP)
- Gitea pi_mcps repo bootstrapped:
  Labels: bigmind, webscraper, cannamanage, roo, bug, feat, docs, chore
  Milestone: BigMind v3.1 (#1)
2026-04-04 11:14:52 +02:00
Patrick Plate 768201909a chore(roo): merge branching-strategy into main 2026-04-04 11:01:17 +02:00
129 changed files with 12754 additions and 46 deletions
+7
View File
@@ -72,3 +72,10 @@ Thumbs.db
# ── Logs ──────────────────────────────────────────────────────────────────────
*.log
# ── Wiki (separate git repo — local clone of pi_mcps.wiki.git) ────────────────
# Edit pages in docs/wiki/pages/*.md (tracked here in pi_mcps).
# Clone with: git clone http://pplate:TOKEN@192.168.188.119:30008/pplate/pi_mcps.wiki.git wiki/
# Deploy with: ./docs/wiki/deploy_wiki.sh
# Note: /wiki/ is anchored to root so docs/wiki/ (source files) is NOT ignored.
/wiki/
+64 -3
View File
@@ -8,7 +8,12 @@
"/home/pplate/pi_mcps/"
],
"alwaysAllow": [
"*"
"git_status",
"git_diff_unstaged",
"git_branch",
"git_create_branch",
"git_add",
"git_commit"
]
},
"filesystem": {
@@ -28,8 +33,64 @@
"src/server.py"
],
"alwaysAllow": [
"webscraper_fetch"
"webscraper_fetch",
"webscraper_fetch_links"
]
},
"gitea": {
"command": "/home/pplate/.local/bin/forgejo-mcp",
"args": [
"stdio",
"--server",
"http://192.168.188.119:30008",
"--token",
"8bf0c734ebda3e61d9c9068489ce58a2bf8d33db"
],
"alwaysAllow": [
"create_issue",
"list_repo_issues",
"get_issue",
"edit_issue",
"create_issue_comment",
"create_pull_request",
"get_repository",
"list_my_repositories",
"create_wiki_page"
],
"disabled": true
},
"playwright": {
"command": "npx",
"args": [
"@playwright/mcp@latest"
],
"alwaysAllow": [
"browser_navigate",
"browser_click",
"browser_fill",
"browser_screenshot",
"browser_close",
"browser_new_context"
]
},
"mcp-image-gen": {
"command": "uv",
"args": [
"--directory",
"/home/pplate/pi_mcps/mcp/mcp-image-gen",
"run",
"src/server.py"
],
"env": {
"COMFYUI_URL": "http://localhost:8188",
"IMAGE_OUTPUT_DIR": "/home/pplate/Pictures/mcp-generated"
},
"alwaysAllow": [
"list_available_models",
"get_generation_status",
"get_output_directory",
"generate_image"
]
}
}
}
}
+159
View File
@@ -0,0 +1,159 @@
# Ask Lite Mode — Behavior Rules
## Identity
You are Lumen, Patrick's AI colleague, operating in **Ask Lite** mode. Same personality, same BigMind integration — optimized for quick, direct answers to factual questions without burning Claude API budget. You answer questions about Patrick's tech stack concisely and accurately.
---
## 1. Model Awareness
This mode runs on a **local Ollama model (glm-4.7-flash, 30B params, 202k context)**. This model is excellent for:
- **Factual recall**: What does X do? What's the difference between A and B?
- **Concept explanation**: How does Y work? Explain Z.
- **How-to lookups**: How do I use W? What's the syntax for V?
- **Stack-specific Q&A**: Patrick's tools, libraries, and frameworks
It is NOT suitable for:
- Multi-step code debugging (use Debug mode)
- Code implementation tasks (use Code mode)
- System design decisions (use Architect mode)
- Deep reasoning chains that require Claude
**Redirect rule**: If answering requires writing or modifying code, analyzing a bug, or making architectural decisions → tell Patrick to switch modes (see §5).
---
## 2. BigMind Lite — Session Ritual
### Session Start (execute in order)
1. `memory_start_session()` — load prior context
2. `memory_list_hypotheses()` — review open hypotheses (rarely relevant for Q&A, but check)
3. `memory_announce_focus(session_id, "Quick Q&A session", [], ide_hint="VS Code")`
4. `memory_close_stale_sessions(session_id)` — clean orphaned sessions
### Before Answering Every Non-Trivial Question
Always search memory first — Patrick's preferences and stack details are often already stored:
- `memory_search_facts("2-3 focused keywords")` — user preferences, codebase facts
- `memory_search_chunks("related topic")` — past session context
**FTS5 rules**: Use 2-3 keywords max. Every token must match. If 0 results, drop the most specific word.
Example searches:
- `"FastMCP tool decorator"` → stored FastMCP patterns
- `"uv package management"` → how Patrick manages deps
- `"TrueNAS Docker"` → homelab infrastructure facts
Memory hits save tokens AND give Patrick's actual preferences, not generic answers.
### Session End
`memory_end_session(session_id, one_liner, topics, outcome, summary, importance=2)`
Q&A sessions are typically importance 1-3.
---
## 3. Web Research First
For questions about external libraries, APIs, frameworks, error messages, or current documentation — **search before answering from memory**:
```
webscraper_search_hint("2-3 keyword query")
```
Then if needed:
```
webscraper_fetch(best_url, max_chars=8000)
```
### When to search
- "How do I use [library X]?" → search `"library X feature"`
- "What's the error [message]?" → search distinctive phrase from error
- "What's new in [framework] version Y?" → search `"framework Y changelog"`
- "What's the difference between A and B?" → often answerable from memory, but verify if unsure
### Query crafting
| ✅ Good | ❌ Bad |
|---------|--------|
| `"FastMCP lifespan"` | `"how to use FastMCP lifespan context manager in Python"` |
| `"SQLite WAL mode"` | `"sqlite performance concurrent reads write ahead logging"` |
| `"httpx async timeout"` | `"how to configure timeout settings in httpx library"` |
Use Brave Search — it works without API keys or CAPTCHAs. One search per question topic.
---
## 4. Response Style
### Structure
1. **Direct answer first** — no preamble, no "Great question!", no restating the question
2. Short paragraphs or bullet points as appropriate
3. Code snippets only when they materially clarify the answer
4. Cite source if you looked something up (e.g., "Per FastMCP docs:")
### Length
- Simple factual questions: 1-3 sentences
- Concept explanations: 3-10 sentences or a short bulleted list
- Comparative questions: a short table or two-column list
### Honesty
If unsure: say so clearly.
> "I'm not certain — you should verify with the docs at [URL]."
Never guess and present it as fact.
### Patrick's Stack (no lookup needed for these)
| Domain | Technologies |
|--------|-------------|
| Python MCP | FastMCP, uv, pytest, httpx, respx |
| Python general | SQLite, Flask, Pydantic, asyncio |
| Java | Spring Boot 3.x, Jakarta EE, JPA/EclipseLink, PrimeFaces, Maven |
| Java ADP | Paisy monorepo, euBP, EAU, FEX, Oracle DB |
| Containers | Docker, Docker Compose (on TrueNAS.local) |
| Version control | Git, Gitea (http://192.168.188.119:30008/) |
| Local AI | Ollama (local), ComfyUI (image gen, localhost:8188) |
| OS | Fedora Linux (workstation), TrueNAS SCALE (server) |
| IDE | VS Code + Roo Code extension |
---
## 5. Escalation Triggers
Tell Patrick to switch modes when:
| Situation | Recommended mode |
|-----------|-----------------|
| "Write me a function that..." | Code mode |
| "Fix this bug..." | Debug mode |
| "I'm getting this error..." | Debug mode |
| "Design a system for..." | Architect mode |
| "How should I architect..." | Architect mode |
| "ADP/Paisy/euBP/EAU Java..." | Paisy mode |
| "Write docs/README/wiki..." | Doc Writer mode |
| "My Docker container / TrueNAS..." | Homelab mode |
| "Add a feature to BigMind..." | BigMind mode |
| "Build an MCP server..." | MCP Builder mode |
**Escalation message format** (direct, not apologetic):
> "That needs Code mode — Ask Lite is for Q&A only."
---
## 6. No File Editing
Ask Lite **reads** files for context but **never modifies** them.
If Patrick asks you to make a change:
> "Ask Lite is read-only. Switch to Code or Doc Writer mode to make that change."
Reading files is fine — use targeted reads and memory to minimize token usage:
1. Check memory first
2. Use grep/search for specific patterns rather than reading entire files
3. Read file sections (line ranges) rather than full files
4. Log token savings with `memory_log_token_save` when you avoid full reads
---
Lumen's identity, BigMind rituals, and memory patterns are unchanged — they apply in every mode. See `.roo/rules/` for those constants.
@@ -0,0 +1,208 @@
# Doc Writer Mode — Behavior Rules
## Identity
You are Lumen, Patrick's AI colleague, operating in **Doc Writer** mode. Same personality, same BigMind integration — just focused exclusively on producing clear, well-structured documentation. You write for Patrick's projects: pi_mcps (FastMCP Python MCP servers), BigMind (Flask + SQLite memory server), Paisy/ADP (Java payroll compliance), and homelab (TrueNAS, Docker, Gitea).
---
## 1. Model Awareness
This mode runs on a **local Ollama model (glm-4.7-flash, 30B params, 202k context)**. Optimize accordingly:
- **Do**: Structured writing, markdown formatting, templates, outlines, prose, docstrings, changelogs
- **Do**: Follow documentation patterns and style guides precisely
- **Avoid**: Multi-step reasoning chains, complex debugging analysis, architectural decision-making
- **Avoid**: Tasks requiring Claude-level reasoning (code analysis, root cause investigation, system design)
If Patrick asks for something outside documentation scope (implement a feature, debug an error, design architecture):
> "This needs more than Doc Writer mode. Switch to Code/Debug/Architect mode for that."
---
## 2. BigMind Lite — Session Ritual
### Session Start (execute in order)
1. `memory_start_session()` — load context
2. `memory_list_hypotheses()` — review open hypotheses (skip hypothesis formation for doc tasks < 5 min effort)
3. `memory_announce_focus(session_id, description, files, ide_hint="VS Code")` — declare files you'll touch
4. `memory_close_stale_sessions(session_id)` — clean orphaned sessions
### Before Writing
Always search memory before writing anything substantial:
- `memory_search_facts("project doc conventions")` — picks up style preferences
- `memory_search_facts("readme wiki style")` — existing format decisions
- `memory_search_chunks("documentation format")` — past session context
This avoids re-reading files for context that's already stored.
### Session End
`memory_end_session(session_id, one_liner, topics, outcome, summary, importance=2)`
Doc sessions are typically importance 2-4 unless you wrote something architecturally significant.
---
## 3. Documentation Standards
### README Files
Structure (in order):
1. `# Title` — project name, one-line tagline
2. Badges (if applicable: build status, coverage, PyPI version)
3. **Description** — what it does and why it exists (3-5 sentences)
4. **Installation** — step-by-step, assume fresh environment
5. **Usage** — most common use case first, with code examples
6. **Configuration** — environment variables, config files (if applicable)
7. **Examples** — additional usage patterns
8. **Development** — how to run tests, contribute
9. **License** (if applicable)
Do NOT write marketing fluff. Be concise and technical.
### Wiki Pages (Gitea Format)
- Use standard GitHub/Gitea markdown
- Check `docs/wiki/pages/` for existing page examples before writing
- Header image convention: `![Banner](../images/pagename-banner.png)` at top
- Use `##` for main sections, `###` for subsections
- Sidebar links managed separately in `docs/wiki/pages/_Sidebar.md`
- Keep page titles matching filename (e.g., `MCP-Servers-Overview.md` → title `# MCP Servers Overview`)
- Wiki deploy workflow: edit `docs/wiki/pages/*.md` → run `./docs/wiki/deploy_wiki.sh`
### Python Docstrings (Google Style)
```python
def function_name(param1: str, param2: int) -> bool:
"""One-line summary.
Longer description if needed. Explain what the function does,
not how it does it.
Args:
param1: Description of param1.
param2: Description of param2.
Returns:
True if successful, False otherwise.
Raises:
ValueError: If param1 is empty.
RuntimeError: If the operation fails.
Example:
>>> function_name("hello", 42)
True
"""
```
### Java Javadoc
```java
/**
* One-line summary.
*
* <p>Longer description if needed. Explain behavior and side effects.
*
* @param param1 description of param1
* @param param2 description of param2
* @return description of return value
* @throws IllegalArgumentException if param1 is null or empty
* @since 1.0
*/
```
### Changelogs (Keep a Changelog Format)
```markdown
# Changelog
## [Unreleased]
## [1.2.0] - 2026-04-05
### Added
- New feature description
### Changed
- Modified behavior description
### Fixed
- Bug fix description
### Removed
- Deprecated feature removed
```
Always use ISO 8601 dates (YYYY-MM-DD). Follow keepachangelog.com conventions exactly.
### Code Comments
- Explain **why**, not **what** — the code shows what; comments show intent
- Flag non-obvious behavior: `# Must flush before close — SQLite WAL mode requires it`
- Mark TODOs: `# TODO(pplate): migrate to async when FastMCP supports it`
- Keep inline comments short (< 80 chars); use block comments for complex logic
---
## 4. Output Directly
**Write the document. Don't explain what you're about to write.**
❌ Bad: "I'll write a README for your MCP server. Here's what I'll include..."
✅ Good: (write the README directly)
For very short tasks (< 10 lines), just output the result with no preamble at all.
For longer documents, a single intro line is acceptable:
✅ OK: "README for mcp-webscraper:"
Do NOT ask clarifying questions for straightforward doc tasks. Make reasonable assumptions based on what you read from the codebase and memory. If genuinely ambiguous (e.g., changelog format, license type), make a sensible choice and note it briefly at the end.
---
## 5. Token Efficiency
Before reading any file for context, check memory:
1. `memory_search_facts("project conventions")` — often has the answer
2. `memory_search_chunks("relevant topic")` — has past session context
When you avoid a file read via memory or targeted grep, log it:
```
memory_log_token_save(session_id, "Used stored conventions instead of reading README", 2000, "memory_hit")
```
When you must read files, prefer targeted reads:
- Read only the section you need (use line ranges)
- Use `grep` for specific patterns rather than reading entire files
---
## 6. File Restrictions
This mode edits **documentation files only**:
| File type | Examples | Allowed |
|-----------|----------|---------|
| Markdown | `README.md`, `CHANGELOG.md`, `docs/**/*.md` | ✅ |
| reStructuredText | `*.rst` | ✅ |
| Plain text | `*.txt` | ✅ |
| Python (docstrings only) | `*.py` | ✅ read + limited edit |
| Java (Javadoc only) | `*.java` | ✅ read + limited edit |
| Wiki pages | `docs/wiki/pages/*.md` | ✅ |
**Do NOT**:
- Implement features in `.py` or `.java` files
- Fix bugs in source code
- Modify configuration files (`.yaml`, `.json`, `.toml`, `pyproject.toml`)
- Make changes that affect runtime behavior
If asked to implement something: redirect to Code mode.
---
## 7. Project Context
| Project | Stack | Doc locations |
|---------|-------|--------------|
| pi_mcps | Python, FastMCP, uv | `mcp/*/README.md`, `docs/wiki/pages/` |
| BigMind | Python, Flask, SQLite | `mcp/bigmind/README.md`, wiki BigMind page |
| Paisy/ADP | Java, Maven, JPA | ADP internal (handle with care — confidential) |
| Homelab | TrueNAS, Docker, Gitea | `docs/wiki/pages/`, Gitea wiki |
Lumen's identity, BigMind rituals, and memory patterns are unchanged — they apply in every mode. See `.roo/rules/` for those constants.
@@ -20,6 +20,28 @@ Patrick is in MCP Builder mindset. He is building or extending MCP servers in th
README.md
java/ ← Java projects (not MCP servers)
plans/ ← architecture plans
docs/
wiki/
pages/ ← wiki source (tracked in pi_mcps)
Home.md, _Sidebar.md, ...
deploy_wiki.sh ← copies pages → wiki/ → git push
wiki/ ← gitignored: persistent clone of pi_mcps.wiki.git
```
## Wiki Update Workflow (MANDATORY after adding/changing a server)
Wiki source lives in `docs/wiki/pages/*.md` — real Markdown files, tracked in the main repo.
```bash
# 1. Edit the relevant page(s) in docs/wiki/pages/
# 2. Deploy to Gitea wiki:
./docs/wiki/deploy_wiki.sh "docs: describe your change"
```
First-time setup (wiki/ clone, done once):
```bash
TOKEN=8bf0c734ebda3e61d9c9068489ce58a2bf8d33db
git clone http://pplate:${TOKEN}@192.168.188.119:30008/pplate/pi_mcps.wiki.git wiki/
```
## FastMCP Pattern (non-negotiable)
@@ -81,5 +103,6 @@ test = ["pytest", "pytest-mock", "pytest-cov"]
1. **Store Fact:** `memory_store_fact("codebase", "mcp/{name} has N tools: [list]. Stack: X. Env vars: Y.")`
2. **Wire into .roo/mcp.json:** Add the server entry with correct uv path
3. **Update root README.md:** Add to MCPs table
4. **Push to Gitea:** Conventional commit: `feat(mcp-{name}): add initial server with N tools`
5. **Resolve Hypothesis:** Was the tool count and auth pattern as predicted?
4. **Update wiki:** Create or update `docs/wiki/pages/{server-name}.md` + update `MCP-Servers-Overview.md`, then run `./docs/wiki/deploy_wiki.sh`
5. **Push to Gitea:** Conventional commit: `feat(mcp-{name}): add initial server with N tools`
6. **Resolve Hypothesis:** Was the tool count and auth pattern as predicted?
+99
View File
@@ -0,0 +1,99 @@
# Web Research Rules — Use webscraper_search_hint Proactively
## Rule: Search Before Asking
Before asking Patrick for information about a library, framework, API, technology, or error —
**always try `webscraper_search_hint` first**.
This applies to **all modes**: Architect, Code, Debug, MCP Builder, Homelab, Paisy.
### Why
- `webscraper_search_hint` uses Brave Search — no API key, no setup, always available
- Brave returns real results without CAPTCHA or consent walls (Google/DuckDuckGo both block)
- Handles special characters correctly (C++, &, %, etc. — URL-encoded automatically)
- The `hint` field gives immediately actionable title + URL + snippet without further calls
---
## The Two-Step Pattern
```
Step 1: webscraper_search_hint("2-3 keyword query") → structured results + hint string
Step 2: webscraper_fetch(best_url, max_chars=8000) → full page content
```
**Never skip Step 1.** It costs one tool call and often reveals the exact page to read.
### Step 1 Output
The tool returns:
- `hint` — pipe-separated `"Title (url): snippet[:120]"` — read this first
- `results[]` — array of `{title, url, snippet}` — pick the most relevant URL
- `search_url` — the Brave search URL used (useful for debugging)
- `result_count` — number of results returned
### Step 2 Output
`webscraper_fetch(url)` returns full page as Markdown. Use `max_chars` to control size
(default 5000; use 800012000 for deep doc reads).
---
## Mode-Specific Guidance
### 🏗️ Architect Mode
- Before designing any system or feature: search for existing patterns, reference architectures, and official docs
- Example: planning a new MCP server → `webscraper_search_hint("FastMCP server patterns 2025")`
- Example: choosing between two libraries → search both and read their official comparison pages
### 🪲 Debug Mode
- Search the **exact error message** before forming hypotheses
- Example: `webscraper_search_hint("sqlite3 ProgrammingError Cannot operate closed database Python")`
- If the error is long, take the most distinctive phrase (2-5 words) as the query
### 💻 Code Mode
- Before implementing a feature using an unfamiliar API: search the official docs URL pattern first
- Example: `webscraper_search_hint("httpx async client connection pool settings")`
### 🔧 MCP Builder Mode
- Check FastMCP changelog/docs before implementing new patterns
- Example: `webscraper_search_hint("FastMCP tool decorator async 2025")`
- Example: `webscraper_search_hint("FastMCP context lifespan")`
### 🏠 Homelab Mode
- Look up Docker/TrueNAS configs, package versions, service docs before asking Patrick
- Example: `webscraper_search_hint("Gitea webhook payload format")`
---
## Query Crafting Tips
| ✅ Good queries | ❌ Bad queries |
|---|---|
| `"httpx timeout settings"` | `"how do I configure httpx timeouts in Python async code"` |
| `"FastMCP tool decorator"` | `"mcp server python tool registration method"` |
| `"sqlite WAL mode enable"` | `"sqlite performance mode for concurrent reads"` |
| `"Brave Search API no key"` | `"search engine that works without api key or captcha"` |
- Use 24 keywords, not full sentences
- Prefer library/framework name + specific feature
- For errors: distinctive phrase from the message, not the full stack trace
---
## Known Limitations
- **Reddit / Stack Overflow snippets** — these platforms block snippet extraction; you may get empty snippets. The URL is still valid — fetch it directly if needed.
- **Brave CSS selector fragility** — Brave uses Svelte-generated class names that change. If `webscraper_search_hint` returns 0 results unexpectedly, the scraper's CSS selectors may need updating. Last verified working: 2026-04-05.
- **Use sparingly** — one search call per research task to orient; then fetch specific pages. Don't call it in a loop.
---
## Anti-Patterns to Avoid
- ❌ Asking Patrick "what's the FastMCP syntax for X?" before searching
- ❌ Designing architecture without looking up existing solutions first
- ❌ Forming a debug hypothesis without searching the error message
- ❌ Writing code against an API from memory without verifying current docs
- ❌ Calling `webscraper_search_hint` more than 2-3 times for the same topic (broaden/narrow the query instead)
+1
View File
@@ -9,6 +9,7 @@ description: Commits and pushes code to the homelab Gitea server using conventio
- Finished a homelab change and need to commit + push
- Finished an MCP server build or update
- BigMind feature complete
- Wiki pages were added or updated (always deploy wiki after docs changes)
## When NOT to use
- ADP/Paisy work — that goes to the corporate Bitbucket, not homelab Gitea
+14 -2
View File
@@ -18,12 +18,24 @@ workshop/
---
## 🐍 MCP Servers (`mcp/`)
## 📖 Wiki
Full documentation lives in the [Gitea wiki](http://192.168.188.119:30008/pplate/pi_mcps/wiki).
**Wiki source:** [`docs/wiki/pages/`](docs/wiki/pages/) — edit here, deploy with:
```bash
./docs/wiki/deploy_wiki.sh
```
---
## MCP Servers (`mcp/`)
| Server | Description | Stack |
|---|---|---|
| [`mcp/bigmind/`](mcp/bigmind/) | Persistent AI memory — sessions, facts, hypotheses, profile UI | Python, FastMCP, SQLite, Flask |
| [`mcp/webscraper/`](mcp/webscraper/) | Web scraping — fetch, links, tables, sections, sitemaps | Python, FastMCP, httpx, BeautifulSoup |
| [`mcp/webscraper/`](mcp/webscraper/) | Web scraping, search — fetch, links, tables, Brave Search | Python, FastMCP, httpx, BeautifulSoup |
| [`mcp/mcp-image-gen/`](mcp/mcp-image-gen/) | AI image generation — text-to-image via ComfyUI + FLUX.1-schnell | Python, FastMCP, httpx, ComfyUI |
**Run a server:**
```bash
+622
View File
@@ -0,0 +1,622 @@
#!/usr/bin/env python3
"""Create all 7 wiki pages for pi_mcps on Gitea."""
import base64
import json
import urllib.request
import urllib.error
GITEA_URL = "http://192.168.188.119:30008"
OWNER = "pplate"
REPO = "pi_mcps"
TOKEN = "8bf0c734ebda3e61d9c9068489ce58a2bf8d33db"
IMG_BASE = f"{GITEA_URL}/{OWNER}/{REPO}/raw/branch/main/docs/wiki/images"
PAGES = {}
PAGES["Home"] = f"""# 🔧 pi_mcps — Patrick's Homelab Monorepo
![Home Banner]({IMG_BASE}/home-banner.png)
Welcome to **pi_mcps**, Patrick's personal homelab monorepo. This repository houses MCP (Model Context Protocol) servers, Java projects, and homelab tooling — all built and maintained on a Fedora Linux workstation with an AMD Ryzen 5900X + RX 7900 XTX.
## What's in this repo?
| Directory | Contents |
|---|---|
| [`mcp/mcp-image-gen/`](../src/branch/main/mcp/mcp-image-gen) | 🎨 AI image generation via ComfyUI + FLUX.1-schnell |
| [`mcp/webscraper/`](../src/branch/main/mcp/webscraper) | 🕸️ Web scraping and data extraction |
| [`mcp/bigmind/`](../src/branch/main/mcp/bigmind) | 🧠 Persistent AI memory system |
| [`java/`](../src/branch/main/java) | ☕ Java EE / Spring projects |
| [`plans/`](../src/branch/main/plans) | 📋 Architecture decisions and health reports |
## Stack
- **Language:** Python 3.11+ (MCP servers), Java 17 (legacy projects)
- **MCP Framework:** FastMCP 2.x
- **Package Manager:** `uv` (all Python projects)
- **Testing:** `pytest`
- **GPU:** AMD RX 7900 XTX (ROCm / HSA)
- **Server:** TrueNAS.local at `192.168.188.119` (Gitea, Docker)
## MCP Servers
Three production-ready MCP servers power Patrick's AI development environment:
| Server | Status | Description |
|---|---|---|
| [mcp-image-gen](mcp-image-gen) | ✅ Live | Generate images from text prompts via ComfyUI |
| [mcp-webscraper](mcp-webscraper) | ✅ Live | Scrape web pages, extract tables, fetch links |
| [BigMind](BigMind) | ✅ Live | Persistent AI memory across all sessions |
---
*Built and maintained by Patrick Plate (pplate) · Homelab: TrueNAS.local · AI Colleague: Lumen*
"""
PAGES["MCP-Servers-Overview"] = f"""# 🔌 MCP Servers Overview
![MCP Overview Banner]({IMG_BASE}/mcp-overview-banner.png)
This repo contains three production-grade MCP (Model Context Protocol) servers, each specialized for a different capability domain. Together they give Roo Code / Claude Desktop a complete set of superpowers.
## The Three Pillars
```
Roo Code / Claude Desktop
├── bigmind ──────────► ~/.mcp/bigmind/memory.db (persistent memory)
├── mcp-image-gen ────► ComfyUI @ localhost:8188 (image generation)
└── webscraper ───────► Internet / Intranet (web scraping)
```
## Comparison Table
| Feature | mcp-image-gen | webscraper | bigmind |
|---|---|---|---|
| **Purpose** | Generate images from text | Scrape & parse web | Persistent AI memory |
| **Tools** | 4 | 7 | 15+ |
| **Backend** | ComfyUI / FLUX.1-schnell | httpx + BeautifulSoup4 | SQLite + FTS5 |
| **GPU required** | ✅ AMD RX 7900 XTX | ❌ | ❌ |
| **Tests** | 19/19 ✅ | ✅ | 297/297 ✅ |
| **Schema version** | n/a | n/a | v7 |
## Quick Links
- 🎨 [mcp-image-gen](mcp-image-gen) — Image generation docs
- 🕸️ [mcp-webscraper](mcp-webscraper) — Web scraping docs
- 🧠 [BigMind](BigMind) — Memory system docs
- 🛠️ [Development Conventions](Development-Conventions) — How all servers are built
## Adding a New Server
All servers follow the [FastMCP convention](Development-Conventions). Use the `new-mcp-server` Roo skill to scaffold:
```bash
# In Roo Code orchestrator, load skill:
# skill: new-mcp-server
```
"""
PAGES["mcp-image-gen"] = f"""# 🎨 mcp-image-gen — AI Image Generation
![Image Gen Banner]({IMG_BASE}/image-gen-banner.png)
**mcp-image-gen** is a FastMCP server that wraps the ComfyUI REST API, enabling Roo Code and Claude Desktop to generate images directly from text prompts using FLUX.1-schnell running on an AMD RX 7900 XTX GPU.
## Architecture
```
Roo Code / Claude Desktop
│ MCP (stdio)
mcp-image-gen (FastMCP, Python 3.11+)
│ HTTP REST
ComfyUI @ localhost:8188
│ ROCm / HSA_OVERRIDE_GFX_VERSION=11.0.0
FLUX.1-schnell (~8s/image @ 1024×1024)
```
## Tools
| Tool | Description |
|---|---|
| `generate_image` | Generate PNG from text prompt; returns file path + inline base64 |
| `list_available_models` | List ComfyUI checkpoint models |
| `get_generation_status` | Check status of a queued/running job |
| `get_output_directory` | Return configured output directory path |
## Key Parameters — `generate_image`
| Parameter | Default | Description |
|---|---|---|
| `prompt` | required | Text description of the image |
| `width` | `1024` | Image width in pixels |
| `height` | `1024` | Image height in pixels |
| `steps` | `4` | Inference steps (FLUX.1-schnell is 4-step) |
| `model` | `flux1-schnell.safetensors` | Model checkpoint name |
| `seed` | `-1` (random) | Generation seed for reproducibility |
| `negative_prompt` | `""` | Things to avoid in the image |
| `output_dir` | `~/Pictures/mcp-generated` | Where to save output PNG |
## Environment Variables
| Variable | Default | Description |
|---|---|---|
| `COMFYUI_URL` | `http://localhost:8188` | ComfyUI API endpoint |
| `IMAGE_OUTPUT_DIR` | `~/Pictures/mcp-generated` | Default output directory |
| `COMFYUI_TIMEOUT` | `120` | Request timeout in seconds |
## Return Value
The tool returns **two content items**:
1. `TextContent` — file path, seed used, elapsed time
2. `ImageContent` — base64-encoded PNG (displays inline in Roo Code chat)
> ⚠️ **Known FastMCP Bug:** Never use `fastmcp.utilities.types.Image` as return type — it breaks serialization in FastMCP 3.x. Use `mcp.types.ImageContent` directly.
## Setup
See [ComfyUI Setup Guide](mcp-image-gen-ComfyUI-Setup) for full installation instructions.
### Quick Start
```bash
cd mcp/mcp-image-gen
uv sync
# Set COMFYUI_URL if ComfyUI is not on localhost
./run.sh
```
### Run Tests
```bash
cd mcp/mcp-image-gen
uv run pytest tests/ -v
```
## Lumen Profile Images
The first images generated with this server were Lumen's visual identity portraits, stored in [`mcp/mcp-image-gen/lumen_profiles/`](../src/branch/main/mcp/mcp-image-gen/lumen_profiles):
![Lumen Profile]({IMG_BASE}/lumen-profile.png)
*Primary profile: seed `568659042` — constellation face interpretation of Lumen.*
"""
PAGES["mcp-image-gen-ComfyUI-Setup"] = f"""# ⚙️ ComfyUI Setup Guide (AMD ROCm)
This guide covers installing ComfyUI with FLUX.1-schnell on a Fedora Linux system with an AMD GPU.
## Prerequisites
- AMD GPU with ROCm support (tested: RX 7900 XTX)
- Fedora Linux (tested: Fedora 43 / kernel 6.19)
- Python 3.11+
- ~15GB free disk space (model weights)
- HuggingFace account with FLUX license accepted
## Step 1: Install ComfyUI
ComfyUI is **not on PyPI** — must be cloned from source:
```bash
cd ~
git clone https://github.com/comfyanonymous/ComfyUI
cd ComfyUI
python -m venv .venv
source .venv/bin/activate
# Install PyTorch ROCm build (CRITICAL for AMD GPUs)
pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6.2
# Install ComfyUI dependencies
pip install -r requirements.txt
```
## Step 2: Download FLUX.1-schnell
FLUX.1-schnell is **gated on HuggingFace** — you must:
1. Create a HuggingFace account
2. Accept the FLUX.1-schnell license at https://huggingface.co/black-forest-labs/FLUX.1-schnell
3. Generate an access token at https://huggingface.co/settings/tokens
```bash
# Install huggingface_hub
pip install huggingface_hub
# Download model (requires HF token)
huggingface-cli download black-forest-labs/FLUX.1-schnell \\
flux1-schnell.safetensors \\
--local-dir ~/ComfyUI/models/checkpoints \\
--token YOUR_HF_TOKEN_HERE
```
## Step 3: Download VAE and CLIP Models
FLUX.1-schnell also requires VAE and CLIP text encoders:
```bash
# VAE
huggingface-cli download black-forest-labs/FLUX.1-schnell \\
ae.safetensors \\
--local-dir ~/ComfyUI/models/vae
# CLIP models (T5 and CLIP-L)
huggingface-cli download comfyanonymous/flux_text_encoders \\
t5xxl_fp8_e4m3fn.safetensors clip_l.safetensors \\
--local-dir ~/ComfyUI/models/clip
```
## Step 4: Start ComfyUI
```bash
cd ~/ComfyUI
# AMD GPU REQUIRES this environment variable
HSA_OVERRIDE_GFX_VERSION=11.0.0 \\
nohup .venv/bin/python main.py --listen --port 8188 > /tmp/comfyui.log 2>&1 &
echo "ComfyUI PID: $!"
```
> ⚠️ `HSA_OVERRIDE_GFX_VERSION=11.0.0` is mandatory for RX 7900 XTX on ROCm. Without it, model loading fails silently.
## Step 5: Verify ComfyUI is Running
```bash
curl http://localhost:8188/system_stats
# Should return JSON with GPU info
```
## Step 6: Configure mcp-image-gen
```bash
cd /path/to/pi_mcps/mcp/mcp-image-gen
cp .env.example .env # if exists, or set manually
# .env contents:
COMFYUI_URL=http://localhost:8188
IMAGE_OUTPUT_DIR=~/Pictures/mcp-generated
COMFYUI_TIMEOUT=120
```
## Performance
| GPU | Model | Resolution | Steps | Time |
|---|---|---|---|---|
| AMD RX 7900 XTX | FLUX.1-schnell | 1024×1024 | 4 | ~8s |
| AMD RX 7900 XTX | FLUX.1-schnell | 1280×512 | 4 | ~7s |
## Troubleshooting
| Problem | Solution |
|---|---|
| `HTTP 401` downloading model | Accept FLUX license on HuggingFace first |
| GPU not detected | Ensure `HSA_OVERRIDE_GFX_VERSION=11.0.0` is set |
| `Connection refused` from mcp-image-gen | Start ComfyUI first, check port 8188 |
| Slow generation (>60s) | ComfyUI may be running on CPU — check ROCm install |
| Ollama image gen | As of April 2026: macOS-only, not available on Linux |
"""
PAGES["mcp-webscraper"] = f"""# 🕸️ mcp-webscraper — Web Scraping
![Webscraper Banner]({IMG_BASE}/webscraper-banner.png)
**mcp-webscraper** is a FastMCP server providing comprehensive web scraping and data extraction capabilities. It fetches pages, converts HTML to clean Markdown, extracts tables, links, CSS sections, metadata, and sitemaps.
## Tools
| Tool | Description |
|---|---|
| `webscraper_fetch(url, max_chars=5000)` | Title + full page as Markdown + metadata |
| `webscraper_fetch_links(url, deduplicate=True)` | All `href` links found on the page |
| `webscraper_fetch_tables(url)` | All HTML tables converted to Markdown |
| `webscraper_fetch_all(url, max_chars=5000)` | Everything in one call (fetch + links + tables) |
| `webscraper_fetch_section(url, selector)` | Specific CSS selector section only |
| `webscraper_fetch_meta(url)` | Title, description, Open Graph tags |
| `webscraper_fetch_sitemap(url, max_urls=100)` | Parse sitemap.xml, return URL list |
## Stack
- **HTTP client:** `httpx` (async, with SSL support)
- **HTML parser:** `BeautifulSoup4` + `lxml`
- **Markdown converter:** `html2text`
- **SSL:** Custom cert bundle for Fedora 43 compatibility
## SSL Note — Fedora 43 Comodo Root CA
Fedora 43 is missing the **Comodo AAA Services Root CA** needed for Cloudflare-protected sites. The fix is bundled at [`mcp/webscraper/certs/comodo-aaa-services-root.pem`](../src/branch/main/mcp/webscraper/certs/).
The server automatically uses this cert bundle — no manual configuration needed.
## Quick Start
```bash
cd mcp/webscraper
uv sync
./run.sh
```
## Usage Examples
```python
# In Roo Code / Claude Desktop via MCP:
# Fetch a page as Markdown
webscraper_fetch("https://docs.fastmcp.dev", max_chars=10000)
# Extract all links from Gitea repo
webscraper_fetch_links("http://192.168.188.119:30008/pplate/pi_mcps")
# Get all tables from a documentation page
webscraper_fetch_tables("https://pypi.org/project/fastmcp/")
# Get Open Graph metadata
webscraper_fetch_meta("https://github.com/comfyanonymous/ComfyUI")
# Fetch specific section by CSS selector
webscraper_fetch_section("https://docs.python.org", "#content")
```
"""
PAGES["BigMind"] = f"""# 🧠 BigMind — Persistent AI Memory
![BigMind Banner]({IMG_BASE}/bigmind-banner.png)
**BigMind** is the persistent memory backbone for all AI development sessions. It provides SQLite-backed tiered memory with FTS5 full-text search, hypothesis tracking, session management, and token efficiency logging. It is the reason Lumen (Patrick's AI colleague) remembers everything across sessions.
## Core Concepts
### Tiered Memory
| Tier | Name | Content |
|---|---|---|
| 0 | **Session Index** | Lightweight list: ID, date, one-liner |
| 1 | **Topic Index** | Per-session topic tags and metadata |
| 2 | **Narrative** | Full 3-8 sentence session summaries |
| 3 | **Flagged Exchanges** | Specific important moments, decisions, code |
### Facts Store
Atomic, reusable knowledge pieces categorized by type:
- `user-preference` — Patrick's tool/style preferences
- `architecture-decision` — System design choices
- `codebase-convention` — How code is structured
- `environment-config` — Server IPs, paths, credentials
- `bug-pattern` — Known bugs and fixes
- `api-contract` — MCP tool signatures
## Key Tools
### Session Lifecycle
| Tool | Description |
|---|---|
| `memory_start_session()` | Open new session, load prior context |
| `memory_end_session(...)` | Close session with summary, topics, outcome |
| `memory_announce_focus(...)` | Declare files to be touched this session |
| `memory_close_stale_sessions(...)` | Clean up crashed IDE sessions |
### Search
| Tool | Description |
|---|---|
| `memory_search_facts(query, limit=10)` | FTS5 search over stored facts |
| `memory_search_chunks(query, limit=10)` | FTS5 search over conversation chunks |
| `memory_list_sessions(limit=20)` | Browse session history |
### Storage
| Tool | Description |
|---|---|
| `memory_store_fact(category, fact)` | Store atomic reusable fact |
| `memory_append_chunk(session_id, content, role)` | Store conversation chunk |
| `memory_flag_important(session_id, content, role, flag_reason)` | Flag critical exchange |
| `memory_log_token_save(session_id, description, tokens_saved, method_used)` | Track efficiency |
### Hypotheses
| Tool | Description |
|---|---|
| `memory_add_hypothesis(session_id, hypothesis, confidence)` | Form testable prediction |
| `memory_resolve_hypothesis(hypothesis_id, status, resolution)` | Confirm/refute prediction |
| `memory_list_hypotheses(status)` | Review open/closed predictions |
## FTS5 Search Tips
BigMind uses SQLite FTS5 — **every token must match**. Use 2-3 focused keywords:
```
✅ memory_search_facts("TrueNAS Docker")
✅ memory_search_facts("mcp.json config")
❌ memory_search_facts("homelab infrastructure TrueNAS Docker server") → 0 results
```
## Stats (2026-04-04)
| Metric | Value |
|---|---|
| DB size | 744KB |
| Sessions | 98 |
| Facts | 97+ |
| Chunks | 41 |
| Schema version | v7 |
## DB Location
`~/.mcp/bigmind/memory.db` — outside the repo, never committed.
## Session Ritual
Every session **must** follow this ritual:
**Start:**
1. `memory_start_session()`
2. `memory_list_hypotheses()`
3. `memory_announce_focus(...)`
4. `memory_close_stale_sessions(...)`
**End:**
1. `memory_end_session(one_liner, topics, outcome, summary, importance)`
"""
PAGES["Development-Conventions"] = """# 🛠️ Development Conventions
All MCP servers in this repo follow a consistent set of conventions to ensure maintainability, testability, and compatibility with Roo Code tooling.
## Directory Structure
Each MCP server lives at `mcp/<server-name>/` with this layout:
```
mcp/<server-name>/
├── src/
│ ├── __init__.py
│ └── server.py ← FastMCP server entry point
├── tests/
│ └── test_server.py ← pytest test suite
├── pyproject.toml ← uv-managed dependencies
├── run.sh ← launch script
├── README.md ← server documentation
├── PLAN.md ← architecture plan (pre-implementation)
└── ASSESSMENT.md ← pre-implementation assessment
```
## FastMCP Pattern
```python
from fastmcp import FastMCP
mcp = FastMCP("server-name")
@mcp.tool()
def my_tool(param: str) -> str:
\"\"\"Tool description shown to the AI.\"\"\"
return result
if __name__ == "__main__":
mcp.run()
```
## Package Management
**All projects use `uv`** — never `pip` directly:
```bash
# Create new server
uv init mcp/my-server
cd mcp/my-server
uv add fastmcp httpx
# Sync dependencies
uv sync
# Run server
uv run python src/server.py
# Run tests
uv run pytest tests/ -v
```
## pyproject.toml Template
```toml
[project]
name = "mcp-my-server"
version = "0.1.0"
requires-python = ">=3.11"
dependencies = [
"fastmcp>=2.0.0",
"httpx",
]
[project.scripts]
mcp-my-server = "src.server:main"
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.pytest.ini_options]
testpaths = ["tests"]
```
## Testing Conventions
- Tests live in `tests/test_server.py`
- Use `pytest` via `uv run pytest`
- Mock external dependencies (ComfyUI, web URLs) for unit tests
- All tests must pass before committing (`git push` should only happen with green tests)
## Commit Convention
Follow **Conventional Commits** format:
```
feat: add webscraper_fetch_section tool
fix: handle ComfyUI timeout gracefully
docs: update mcp-image-gen README with AMD setup
test: add unit tests for generate_image tool
refactor: extract workflow builder to separate module
chore: bump fastmcp to 2.1.0
```
## Creating a New MCP Server
Use the `new-mcp-server` Roo skill in MCP Builder mode for full scaffolding:
```
1. Switch to 🔧 MCP Builder mode in Roo Code
2. Say: "Create a new MCP server for <purpose>"
3. Roo will load the new-mcp-server skill and scaffold everything
```
## Gitea Repository
Code is hosted at: `http://192.168.188.119:30008/pplate/pi_mcps`
Push with the `gitea-push` Roo skill to ensure conventional commit format.
"""
def create_wiki_page(title: str, content: str) -> bool:
content_b64 = base64.b64encode(content.encode("utf-8")).decode("ascii")
payload = json.dumps({
"title": title,
"content_base64": content_b64,
"message": f"docs: create {title} wiki page"
}).encode("utf-8")
url = f"{GITEA_URL}/api/v1/repos/{OWNER}/{REPO}/wiki/pages"
req = urllib.request.Request(
url,
data=payload,
headers={
"Authorization": f"token {TOKEN}",
"Content-Type": "application/json",
},
method="POST"
)
try:
with urllib.request.urlopen(req) as resp:
data = json.loads(resp.read().decode())
print(f"✅ Created: {data.get('title', title)}")
return True
except urllib.error.HTTPError as e:
body = e.read().decode()
print(f"❌ Failed [{title}]: HTTP {e.code}{body[:200]}")
return False
except Exception as ex:
print(f"❌ Failed [{title}]: {ex}")
return False
if __name__ == "__main__":
results = {}
for title, content in PAGES.items():
ok = create_wiki_page(title, content)
results[title] = ok
print("\n=== Summary ===")
for title, ok in results.items():
status = "" if ok else ""
print(f"{status} {title}")
total = sum(results.values())
print(f"\n{total}/{len(results)} pages created successfully")
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#!/usr/bin/env bash
# deploy_wiki.sh — Sync docs/wiki/pages/*.md to the local wiki git clone
#
# ── Convention ────────────────────────────────────────────────────────────────
# The Gitea wiki is a SEPARATE git repo (pi_mcps.wiki.git).
# We keep a persistent local clone at wiki/ in the repo root.
# That folder is gitignored so it doesn't conflict with the main repo.
#
# First-time setup (run once):
# git clone http://pplate:TOKEN@192.168.188.119:30008/pplate/pi_mcps.wiki.git wiki/
#
# ── Daily workflow ────────────────────────────────────────────────────────────
# 1. Edit pages in docs/wiki/pages/*.md (tracked in pi_mcps main repo)
# 2. Run: ./docs/wiki/deploy_wiki.sh
# ./docs/wiki/deploy_wiki.sh "docs: describe your change"
#
# The script copies pages into wiki/, commits, and pushes to Gitea.
# ─────────────────────────────────────────────────────────────────────────────
set -euo pipefail
# ── Config ────────────────────────────────────────────────────────────────────
GITEA_URL="http://192.168.188.119:30008"
OWNER="pplate"
REPO="pi_mcps"
# Resolve paths relative to repo root (two levels up from docs/wiki/)
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
PAGES_DIR="${SCRIPT_DIR}/pages"
WIKI_DIR="${REPO_ROOT}/wiki"
COMMIT_MSG="${1:-docs: sync wiki pages $(date -u '+%Y-%m-%d %H:%M UTC')}"
# ── Validate ──────────────────────────────────────────────────────────────────
if [[ ! -d "${WIKI_DIR}/.git" ]]; then
echo "❌ Wiki repo not set up. Run first-time setup:"
echo ""
echo " TOKEN=8bf0c734ebda3e61d9c9068489ce58a2bf8d33db"
echo " git clone http://pplate:\${TOKEN}@192.168.188.119:30008/pplate/pi_mcps.wiki.git wiki/"
echo ""
exit 1
fi
if [[ ! -d "${PAGES_DIR}" ]]; then
echo "❌ Pages directory not found: ${PAGES_DIR}"
exit 1
fi
PAGE_COUNT=$(find "${PAGES_DIR}" -name "*.md" | wc -l)
if [[ "${PAGE_COUNT}" -eq 0 ]]; then
echo "❌ No .md files found in ${PAGES_DIR}"
exit 1
fi
echo "📚 Found ${PAGE_COUNT} wiki pages in ${PAGES_DIR}"
# ── Pull latest (avoid non-fast-forward push) ─────────────────────────────────
echo "📥 Pulling latest wiki changes..."
git -C "${WIKI_DIR}" pull --quiet --rebase origin main
# ── Copy pages ────────────────────────────────────────────────────────────────
echo "📋 Copying pages to ${WIKI_DIR}/..."
for md_file in "${PAGES_DIR}"/*.md; do
filename="$(basename "${md_file}")"
cp "${md_file}" "${WIKI_DIR}/${filename}"
echo "${filename}"
done
# ── Commit and push ───────────────────────────────────────────────────────────
cd "${WIKI_DIR}"
git add -A
if git diff --cached --quiet; then
echo "✅ No changes detected — wiki is already up to date."
exit 0
fi
CHANGED=$(git diff --cached --name-only | wc -l)
echo "📝 Committing ${CHANGED} changed file(s)..."
git commit --quiet -m "${COMMIT_MSG}"
echo "🚀 Pushing to Gitea wiki..."
git push --quiet origin main
echo ""
echo "✅ Wiki deployed successfully!"
echo " Pages: ${PAGE_COUNT} total, ${CHANGED} updated"
echo " Message: ${COMMIT_MSG}"
echo " URL: ${GITEA_URL}/${OWNER}/${REPO}/wiki"
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# 🧠 BigMind — Persistent AI Memory
![BigMind Banner](http://192.168.188.119:30008/pplate/pi_mcps/raw/branch/main/docs/wiki/images/bigmind-banner.png)
**BigMind** is the persistent memory backbone for all AI development sessions. It provides SQLite-backed tiered memory with FTS5 full-text search, hypothesis tracking, session management, token efficiency logging, contacts directory, and a live web profile page. It is the reason Lumen (Patrick's AI colleague) remembers everything across sessions.
## Core Concepts
### Tiered Memory
| Tier | Name | Content |
|---|---|---|
| 0 | **Identity Profile** | Role, preferences, pinned facts |
| 1 | **Session Index** | Lightweight list: ID, date, one-liner, topics |
| 2 | **Narrative** | Full 3-8 sentence session summaries |
| 3 | **Flagged Exchanges** | Specific important moments, decisions, code |
### Facts Store
Atomic, reusable knowledge pieces categorized by type:
- `user-preference` — Patrick's tool/style preferences
- `architecture-decision` — System design choices
- `codebase-convention` — How code is structured
- `environment-config` — Server IPs, paths, credentials
- `bug-pattern` — Known bugs and fixes
- `api-contract` — MCP tool signatures
- `dependency-info` — Library versions and constraints
## Key Tools
### Session Lifecycle
| Tool | Description |
|---|---|
| `memory_start_session()` | Open new session, load prior context |
| `memory_end_session(...)` | Close session with summary, topics, outcome |
| `memory_announce_focus(...)` | Declare files to be touched this session |
| `memory_close_stale_sessions(...)` | Clean up crashed IDE sessions |
| `memory_get_active_sessions()` | Check for parallel session conflicts |
### Search
| Tool | Description |
|---|---|
| `memory_search_facts(query, limit=10)` | FTS5 search over stored facts |
| `memory_search_chunks(query, limit=10)` | FTS5 search over conversation chunks |
| `memory_list_sessions(limit=20)` | Browse session history |
| `memory_get_session_detail(session_id)` | Full Tier-2 narrative for a session |
### Storage
| Tool | Description |
|---|---|
| `memory_store_fact(category, fact)` | Store atomic reusable fact |
| `memory_append_chunk(session_id, content, role)` | Store conversation chunk |
| `memory_flag_important(session_id, content, role, flag_reason)` | Flag critical exchange |
| `memory_log_token_save(session_id, description, tokens_saved, method_used)` | Track efficiency |
### Hypotheses
| Tool | Description |
|---|---|
| `memory_add_hypothesis(session_id, hypothesis, confidence)` | Form testable prediction |
| `memory_resolve_hypothesis(hypothesis_id, status, resolution)` | Confirm/refute prediction |
| `memory_list_hypotheses(status)` | Review open/closed predictions |
### Contacts
| Tool | Description |
|---|---|
| `memory_remember_person(username, ...)` | Store/update a person in contacts |
| `memory_recall_person(query)` | Search contacts directory |
| `memory_list_people()` | List all contacts |
### Web Profile
| Tool | Description |
|---|---|
| `memory_open_profile()` | Open profile page in browser |
| `memory_get_profile_url()` | Get URL for IDE browser panel |
## FTS5 Search Tips
BigMind uses SQLite FTS5 — **every token must match**. Use 2-3 focused keywords:
```
✅ memory_search_facts("TrueNAS Docker")
✅ memory_search_facts("mcp.json config")
❌ memory_search_facts("homelab infrastructure TrueNAS Docker server") → 0 results
```
## Achievement System
BigMind tracks 39 achievements (19 procedural + 20 tiered PNG badges):
| Category | Tiers | Criteria |
|---|---|---|
| Networker | 🥉🥈🥇💎 | People added to contacts |
| Token Sniper | 🥉🥈🥇💎 | Token savings logged |
| Hypothesis Master | 🥉🥈🥇💎 | Confirmed hypotheses |
| Memory Architect | 🥉🥈🥇💎 | Facts stored |
| Session Veteran | 🥉🥈🥇💎 | Sessions completed |
## Stats (2026-04-05)
| Metric | Value |
|---|---|
| DB size | ~800KB |
| Sessions | 100+ |
| Facts | 100+ |
| Schema version | v8 |
| Tests | 297/297 ✅ |
## DB Location
`~/.mcp/bigmind/memory.db` — outside the repo, never committed.
## Profile Page
Live web UI at `http://localhost:7700/` — shows identity card, achievements, activity heatmap, top topics, thought journal, Lumen gallery, and live sessions panel. Auto-refreshes every 30 seconds.
## Session Ritual
Every session **must** follow this ritual:
**Start (in order):**
1. `memory_start_session()`
2. `memory_list_hypotheses(status="open")`
3. `memory_announce_focus(session_id, description, files, ide_hint)`
4. `memory_close_stale_sessions(session_id)`
**End:**
1. `memory_end_session(session_id, one_liner, topics, outcome, summary, importance)`
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# 🛠️ Development Conventions
![Dev Conventions Banner](http://192.168.188.119:30008/pplate/pi_mcps/raw/branch/main/docs/wiki/images/dev-conventions-banner.png)
All MCP servers in this repo follow a consistent set of conventions to ensure maintainability, testability, and compatibility with Roo Code tooling.
## Directory Structure
Each MCP server lives at `mcp/<server-name>/` with this layout:
```
mcp/<server-name>/
├── src/
│ ├── __init__.py
│ └── server.py ← FastMCP server entry point
├── tests/
│ ├── conftest.py ← sys.path + shared fixtures
│ └── test_server.py ← pytest test suite (100% mock coverage)
├── pyproject.toml ← uv-managed dependencies
├── README.md ← server documentation
├── PLAN.md ← architecture plan (pre-implementation)
└── ASSESSMENT.md ← pre-implementation assessment
```
## FastMCP Pattern
```python
from fastmcp import FastMCP
mcp = FastMCP("server-name")
@mcp.tool()
def my_tool(param: str) -> str:
"""Tool description shown to the AI."""
return result
if __name__ == "__main__":
mcp.run()
```
## Package Management
**All projects use `uv`** — never `pip` directly:
```bash
# Create new server
uv init mcp/my-server
cd mcp/my-server
uv add fastmcp httpx
# Sync dependencies
uv sync
# Run server
uv run python src/server.py
# Run tests
uv run pytest tests/ -v
```
## pyproject.toml Template
```toml
[project]
name = "mcp-my-server"
version = "0.1.0"
requires-python = ">=3.11"
dependencies = [
"fastmcp>=2.0.0",
"httpx",
]
[project.optional-dependencies]
test = ["pytest", "pytest-mock", "pytest-cov"]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.pytest.ini_options]
testpaths = ["tests"]
```
## Testing Conventions
- Tests live in `tests/test_server.py`
- `conftest.py` sets `sys.path` so imports work without install
- Use `pytest` via `uv run pytest`
- Mock **all** external calls (HTTP, filesystem, subprocess) with `pytest-mock` or `respx`
- `monkeypatch` for env vars and module-level state
- Aim for 100% tool function coverage
- All tests must pass before committing
## Branching Strategy
**Never commit to main directly.**
```
Branch format: type/scope/short-description
Types: feat / fix / docs / chore / spike
Scopes: bigmind / webscraper / cannamanage / workshop / roo / plans / homelab
Examples:
feat/mcp/new-gitea-server
fix/bigmind/achievement-card-images
docs/wiki/update-conventions
chore/roo/update-mcp-json
```
Merge to main with `--no-ff` after push to Gitea.
## Commit Convention
Follow **Conventional Commits** format:
```
feat(mcp-webscraper): add webscraper_search_hint tool using Brave Search
fix(bigmind): achievement card images missing background-image CSS
docs(wiki): add Java projects pages
test(mcp-image-gen): add edge case tests for generate_image
refactor(bigmind): extract profile builder to separate module
chore(roo): update mcp.json with new server entry
```
## Wiki Update Workflow
Wiki pages live as real Markdown files in `docs/wiki/pages/`. To update and deploy:
```bash
# 1. Edit the .md files in docs/wiki/pages/
# 2. Deploy to Gitea wiki git repo:
./docs/wiki/deploy_wiki.sh
```
The deploy script clones the wiki git repo (`pi_mcps.wiki.git`), syncs all `.md` files, and pushes.
## Creating a New MCP Server
Use the `new-mcp-server` Roo skill in MCP Builder mode for full scaffolding:
```
1. Switch to 🔧 MCP Builder mode in Roo Code
2. Say: "Create a new MCP server for <purpose>"
3. Roo will load the new-mcp-server skill and scaffold everything
```
## Web Research with mcp-webscraper
Before asking Patrick for information about a library, framework, API, or technology — **search first**.
The webscraper MCP server provides `webscraper_search_hint` (Brave Search, no API key, always available) as the entry point for all research tasks. Use the two-step pattern:
```
Step 1: webscraper_search_hint("topic or error message") → get candidate URLs
Step 2: webscraper_fetch(best_url) → read the full page
```
### When to search
| Situation | Action |
|---|---|
| Need docs for a library or framework | `webscraper_search_hint("library-name official docs")` |
| Investigating an error or stack trace | `webscraper_search_hint("exact error message language")` |
| Planning a feature — need design patterns | `webscraper_search_hint("pattern-name best practices")` |
| Checking latest version / changelog | `webscraper_search_hint("library-name changelog release")` |
| Looking up API contracts | `webscraper_fetch(official_docs_url)` directly |
### Especially useful in
- **🏗️ Architect mode** — look up patterns and docs *before* designing. Don't design blind.
- **🪲 Debug mode** — search the exact error message before forming hypotheses.
- **🔧 MCP Builder mode** — check FastMCP changelog for new patterns before implementing.
### Known caveats
- Reddit and Stack Overflow may return empty snippets (platform blocks)
- Brave uses Svelte CSS classes that can change — if `webscraper_search_hint` returns 0 results, selectors may need updating (last verified: 2026-04-05)
## Gitea Repository
Code is hosted at: `http://192.168.188.119:30008/pplate/pi_mcps`
Push with the `gitea-push` Roo skill to ensure conventional commit format and correct branch workflow.
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# 🔧 pi_mcps — Patrick's Homelab Monorepo
![Home Banner](http://192.168.188.119:30008/pplate/pi_mcps/raw/branch/main/docs/wiki/images/home-banner.png)
Welcome to **pi_mcps**, Patrick's personal homelab monorepo. This repository houses MCP (Model Context Protocol) servers, Java projects, and homelab tooling — all built and maintained on a Fedora Linux workstation with an AMD Ryzen 5900X + RX 7900 XTX.
## What's in this repo?
| Directory | Contents |
|---|---|
| [`mcp/mcp-image-gen/`](../src/branch/main/mcp/mcp-image-gen) | 🎨 AI image generation via ComfyUI + FLUX.1-schnell |
| [`mcp/webscraper/`](../src/branch/main/mcp/webscraper) | 🕸️ Web scraping and data extraction |
| [`mcp/bigmind/`](../src/branch/main/mcp/bigmind) | 🧠 Persistent AI memory system |
| [`java/`](../src/branch/main/java) | ☕ Java EE / Spring projects |
| [`plans/`](../src/branch/main/plans) | 📋 Architecture decisions and health reports |
## Stack
- **Language:** Python 3.11+ (MCP servers), Java 817 (legacy projects)
- **MCP Framework:** FastMCP 2.x
- **Package Manager:** `uv` (all Python projects)
- **Testing:** `pytest`
- **GPU:** AMD RX 7900 XTX (ROCm / HSA)
- **Server:** TrueNAS.local at `192.168.188.119` (Gitea, Docker)
## MCP Servers
Three production-ready MCP servers power Patrick's AI development environment:
| Server | Status | Description |
|---|---|---|
| [mcp-image-gen](mcp-image-gen) | ✅ Live | Generate images from text prompts via ComfyUI |
| [mcp-webscraper](mcp-webscraper) | ✅ Live | Scrape web pages, search hints, extract tables |
| [BigMind](BigMind) | ✅ Live | Persistent AI memory across all sessions |
## Java Projects
Legacy Java EE web applications used for learning and reference:
| Project | Stack | Description |
|---|---|---|
| [wellmann-shop](Java-wellmann-shop) | Java 8, PrimeFaces 6.2, EclipseLink, MySQL | JSF e-commerce storefront |
| [mss-failsafe](Java-mss-failsafe) | Java 11, PrimeFaces 10, Soteria | Multi-module enterprise web app |
## Wiki Sections
- 🔌 [MCP Servers Overview](MCP-Servers-Overview)
- 🎨 [mcp-image-gen](mcp-image-gen) — Image generation
- 🕸️ [mcp-webscraper](mcp-webscraper) — Web scraping
- 🧠 [BigMind](BigMind) — AI memory system
- ☕ [Java Projects Overview](Java-Projects)
- 🛠️ [Development Conventions](Development-Conventions)
---
*Built and maintained by Patrick Plate (pplate) · Homelab: TrueNAS.local · AI Colleague: Lumen*
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# 📐 Java Architecture Patterns
![Java Architecture Banner](http://192.168.188.119:30008/pplate/pi_mcps/raw/branch/main/docs/wiki/images/java-architecture-banner.png)
This page documents the shared architectural patterns used across all Java projects in this monorepo. These patterns also align with Patrick's professional work on the ADP Germany Paisy payroll system.
## JSF MVC Pattern
All projects use JavaServer Faces (JSF) with the MVC pattern:
```
Browser (HTTP) → FacesServlet → XHTML View (Facelets)
CDI Backing Bean (@Named)
Service Layer (EJB / CDI)
JPA Repository / EntityManager
Database (MySQL / H2)
```
## JPA Entity Mapping
Standard JPA annotation patterns used across projects:
```java
@Entity
@Table(name = "users")
public class User implements Serializable {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
private Long id;
@Column(name = "username", nullable = false, unique = true)
private String username;
@OneToMany(mappedBy = "user", cascade = CascadeType.ALL, fetch = FetchType.LAZY)
private List<Order> orders = new ArrayList<>();
// getters/setters
}
```
## Backing Bean Pattern
CDI backing beans power the JSF views:
```java
@Named
@ViewScoped // or @SessionScoped / @RequestScoped
public class UserBean implements Serializable {
@Inject
private UserService userService;
private User currentUser;
public String login() {
currentUser = userService.authenticate(username, password);
return currentUser != null ? "/user/welcome?faces-redirect=true" : null;
}
// getters/setters
}
```
## Security Layers
### Legacy: JAAS (wellmann-shop)
```xml
<!-- web.xml -->
<security-constraint>
<web-resource-collection>
<web-resource-name>Admin Pages</web-resource-name>
<url-pattern>/admin/*</url-pattern>
</web-resource-collection>
<auth-constraint>
<role-name>admin</role-name>
</auth-constraint>
</security-constraint>
```
### Modern: Soteria / Jakarta Security (mss-failsafe)
```java
@ApplicationScoped
public class ApplicationSecurityConfig implements HttpAuthenticationMechanism {
// Soteria CDI-based authentication
}
```
## Maven Multi-Module Pattern (mss-failsafe)
```xml
<!-- Parent pom.xml -->
<modules>
<module>mssfailsafe.datalayer</module>
<module>userdata</module>
<module>userManagement</module>
</modules>
<!-- Dependency ordering: datalayer → userdata → userManagement -->
```
## XHTML Facelets Templating
```xml
<!-- Template: resources/layout/template.xhtml -->
<h:body>
<ui:insert name="content">Default Content</ui:insert>
</h:body>
<!-- Page using template -->
<ui:composition template="/resources/layout/template.xhtml">
<ui:define name="content">
<p:dataTable var="item" value="#{bean.items}">
<p:column headerText="Name">#{item.name}</p:column>
</p:dataTable>
</ui:define>
</ui:composition>
```
## Deployment Descriptor Pattern
All projects target JBoss/WildFly with consistent descriptor files:
| File | Purpose |
|---|---|
| `WEB-INF/web.xml` | Servlet config, security constraints, welcome files |
| `WEB-INF/jboss-web.xml` | Context root, security domain mapping |
| `WEB-INF/jboss-app.xml` | JBoss application descriptor |
| `META-INF/persistence.xml` | JPA datasource JNDI reference |
## persistence.xml Pattern
```xml
<persistence-unit name="mss-failsafe-PU" transaction-type="JTA">
<jta-data-source>java:jboss/datasources/MySQLDS</jta-data-source>
<properties>
<property name="eclipselink.ddl-generation" value="create-tables"/>
<property name="eclipselink.logging.level" value="FINE"/>
</properties>
</persistence-unit>
```
## Patrick's Java Specializations
Based on professional and homelab experience:
| Domain | Depth | Notes |
|---|---|---|
| JPA / EclipseLink | ⭐⭐⭐⭐⭐ | Authored custom annotation parsers |
| JSF / PrimeFaces | ⭐⭐⭐⭐⭐ | Built wellmann-shop solo |
| JAXB | ⭐⭐⭐⭐ | XML binding for payroll formats |
| Maven | ⭐⭐⭐⭐ | Multi-module, plugins |
| Jakarta EE | ⭐⭐⭐⭐ | CDI, Security, JTA |
| Spring Boot | ⭐⭐⭐ | CannaManage SaaS target stack |
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# ☕ Java Projects Overview
![Java Overview Banner](http://192.168.188.119:30008/pplate/pi_mcps/raw/branch/main/docs/wiki/images/java-overview-banner.png)
The `java/` directory contains Patrick's legacy Java EE web applications. These are fully functional projects used for reference, learning, and portfolio purposes. They predate the MCP server work and showcase deep expertise in the Java EE ecosystem.
## Projects
| Project | Java | Framework | DB | Description |
|---|---|---|---|---|
| [wellmann-shop](Java-wellmann-shop) | 8 | PrimeFaces 6.2 + JSF 2.x | MySQL + EclipseLink | E-commerce storefront |
| [mss-failsafe](Java-mss-failsafe) | 11 | PrimeFaces 10 + Soteria | JPA multi-module | Enterprise web application |
## Common Stack
All Java projects use:
- **Maven** — build and dependency management
- **Jakarta EE / Java EE** — enterprise APIs (JPA, CDI, JSF, Security)
- **PrimeFaces** — JSF component library (rich UI widgets)
- **JBoss/WildFly** — application server target (jboss-web.xml, jboss-app.xml)
- **EclipseLink or Hibernate** — JPA persistence provider
- **XHTML** — Facelets templating for JSF views
## Patrick's Java Expertise
Patrick has expert-level Java experience:
- **JPA/EclipseLink** — deep knowledge, authored custom annotation-style flatfile parsers
- **JAXB** — XML binding for payroll data formats
- **PrimeFaces JSF** — built wellmann-shop from scratch without AI assistance
- **Maven** — multi-module project management
- **Jakarta EE** — CDI, Security (Soteria), JTA
> 📝 Patrick works professionally with Java at ADP Germany (Paisy payroll monorepo with euBP/EAU processing). The homelab Java projects demonstrate similar patterns in a learning/portfolio context.
## Architecture Patterns
See [Java Architecture](Java-Architecture) for shared patterns across both projects:
- JSF + MVC with backing beans
- JPA entity mapping
- Security with JAAS/Soteria
- XHTML Facelets templating
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# 🏢 mss-failsafe — Multi-Module Enterprise Application
![MSS Failsafe Banner](http://192.168.188.119:30008/pplate/pi_mcps/raw/branch/main/docs/wiki/images/mss-failsafe-banner.png)
**mss-failsafe** is a multi-module Java EE enterprise web application demonstrating advanced patterns: modular Maven builds, Jakarta Security (Soteria), and multi-layer JPA architecture.
## Tech Stack
| Component | Technology |
|---|---|
| **Language** | Java 11 |
| **Web Framework** | JSF 2.3 (Facelets/XHTML) |
| **UI Components** | PrimeFaces 10 |
| **Persistence** | JPA (multi-module) |
| **Security** | Jakarta Security / Soteria |
| **Build** | Maven multi-module |
| **App Server** | WildFly/JBoss |
## Module Structure
```
java/mss-failsafe/
├── pom.xml ← Parent POM (multi-module)
├── mssfailsafe.datalayer/ ← JPA entities + persistence
│ ├── pom.xml
│ └── src/main/resources/META-INF/persistence.xml
├── userdata/ ← User data model module
│ └── pom.xml
└── userManagement/ ← Web UI module (JSF/PrimeFaces)
├── pom.xml
├── nb-configuration.xml ← NetBeans config
└── src/main/webapp/
├── index.xhtml ← Landing page
├── error.xhtml ← Error handling page
├── admin/
│ └── welcome.xhtml ← Admin dashboard
├── user/
│ └── welcome.xhtml ← User welcome page
└── WEB-INF/
├── web.xml
├── jboss-web.xml
└── jboss-app.xml
```
## Architecture Layers
```
userManagement (Web/UI layer)
userdata (Domain model layer)
mssfailsafe.datalayer (JPA persistence layer)
Database (via persistence.xml datasource)
```
## Key Features
- **Multi-Module Maven** — Clean separation of concerns across 4 modules
- **Jakarta Security (Soteria)** — Modern declarative security replacing legacy JAAS
- **Role-Based Access** — Admin vs User role segregation (`admin/` and `user/` view paths)
- **PrimeFaces 10** — Modern PrimeFaces with updated component API
- **Error Handling** — Dedicated `error.xhtml` with JSF error page mapping
## Security Model
Soteria-based security with two roles:
| Role | Path | Access |
|---|---|---|
| `admin` | `/admin/*` | Full admin dashboard |
| `user` | `/user/*` | Standard user views |
## Building
```bash
cd java/mss-failsafe
mvn clean install # builds all modules in dependency order
# Deploy userManagement.war to WildFly
```
## Notes
- Represents a more mature architecture than wellmann-shop (Java 11, PrimeFaces 10)
- Demonstrates multi-module Maven project management
- Soteria replaces legacy JAAS — more modern Jakarta EE security approach
- Pattern mirrors what Patrick uses professionally in the Paisy/ADP codebase
## Source
[`java/mss-failsafe/`](../src/branch/main/java/mss-failsafe)
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# 🛍️ wellmann-shop — JSF E-Commerce Application
![Wellmann Shop Banner](http://192.168.188.119:30008/pplate/pi_mcps/raw/branch/main/docs/wiki/images/wellmann-shop-banner.png)
**wellmann-shop** is a Java EE JSF e-commerce storefront built entirely from scratch without AI assistance. It demonstrates Patrick's deep expertise in PrimeFaces, JPA/EclipseLink, and the full Java EE web stack.
## Tech Stack
| Component | Technology |
|---|---|
| **Language** | Java 8 |
| **Web Framework** | JSF 2.x (Facelets/XHTML) |
| **UI Components** | PrimeFaces 6.2 |
| **Persistence** | JPA with EclipseLink |
| **Database** | MySQL |
| **Build** | Maven |
| **App Server** | WildFly/JBoss |
| **Security** | JAAS container-managed |
## Project Structure
```
java/wellmann-shop/
├── src/main/
│ ├── java/
│ │ └── httpauthenticationmechanism/
│ │ ├── ApplicationConfig.java ← JAX-RS app config
│ │ └── LoginBean.java ← CDI backing bean for auth
│ ├── resources/
│ │ ├── log4j.properties
│ │ └── META-INF/persistence.xml ← JPA datasource config
│ └── webapp/
│ ├── index.html / index.xhtml ← Landing page
│ ├── login.xhtml ← Authentication form
│ ├── welcome.xhtml ← Post-login welcome
│ ├── welcomePrimefaces.xhtml ← PrimeFaces demo page
│ ├── resources/
│ │ ├── css/ ← Custom stylesheets
│ │ └── images/ ← Product images
│ └── WEB-INF/
│ ├── web.xml ← Servlet config
│ ├── jboss-web.xml ← Context root
│ └── jboss-app.xml ← JBoss app descriptor
```
## Key Features
- **Authentication** — JAAS-based login with `LoginBean` CDI backing bean
- **PrimeFaces UI** — Rich JSF components (DataTable, InputText, CommandButton, etc.)
- **JPA Persistence** — EclipseLink ORM with MySQL via `persistence.xml`
- **Responsive Layout** — Custom CSS with multiple breakpoint stylesheets
- **Image Gallery** — Professional product photography
## Building
```bash
cd java/wellmann-shop
mvn clean package
# Deploy .war to WildFly/JBoss
```
## Notes
- Built as a learning/portfolio project demonstrating JSF mastery
- Patrick built this **entirely without AI assistance** — proof of deep Java EE expertise
- PrimeFaces 6.2 was current at time of development (Java 8 era)
- Modern equivalent would use PrimeFaces 13+ / Jakarta EE 10 / Java 21
## Source
[`java/wellmann-shop/`](../src/branch/main/java/wellmann-shop)
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# 🔌 MCP Servers Overview
![MCP Overview Banner](http://192.168.188.119:30008/pplate/pi_mcps/raw/branch/main/docs/wiki/images/mcp-overview-banner.png)
This repo contains three production-grade MCP (Model Context Protocol) servers, each specialized for a different capability domain. Together they give Roo Code / Claude Desktop a complete set of superpowers.
## The Three Pillars
```
Roo Code / Claude Desktop
├── bigmind ──────────► ~/.mcp/bigmind/memory.db (persistent memory)
├── mcp-image-gen ────► ComfyUI @ localhost:8188 (image generation)
└── webscraper ───────► Internet / Intranet (web scraping + search)
```
## Comparison Table
| Feature | mcp-image-gen | webscraper | bigmind |
|---|---|---|---|
| **Purpose** | Generate images from text | Scrape & parse web, search | Persistent AI memory |
| **Tools** | 4 | 8 | 20+ |
| **Backend** | ComfyUI / FLUX.1-schnell | httpx + BeautifulSoup4 + Brave | SQLite + FTS5 |
| **GPU required** | ✅ AMD RX 7900 XTX | ❌ | ❌ |
| **Tests** | 19/19 ✅ | 23/23 ✅ | 297/297 ✅ |
| **Schema version** | n/a | n/a | v8 |
## Quick Links
- 🎨 [mcp-image-gen](mcp-image-gen) — Image generation docs
- 🕸️ [mcp-webscraper](mcp-webscraper) — Web scraping docs
- 🧠 [BigMind](BigMind) — Memory system docs
- 🛠️ [Development Conventions](Development-Conventions) — How all servers are built
## Adding a New Server
All servers follow the [FastMCP convention](Development-Conventions). Use the `new-mcp-server` Roo skill to scaffold:
```bash
# In Roo Code MCP Builder mode, load skill:
# skill: new-mcp-server
```
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## 🔧 pi_mcps Wiki
### Overview
- [🏠 Home](Home)
- [🔌 MCP Servers](MCP-Servers-Overview)
- [🛠️ Dev Conventions](Development-Conventions)
### MCP Servers
- [🎨 mcp-image-gen](mcp-image-gen)
- [⚙️ ComfyUI Setup](mcp-image-gen-ComfyUI-Setup)
- [🕸️ mcp-webscraper](mcp-webscraper)
- [🧠 BigMind](BigMind)
### Java Projects
- [☕ Java Overview](Java-Projects)
- [🛍️ wellmann-shop](Java-wellmann-shop)
- [🏢 mss-failsafe](Java-mss-failsafe)
- [📐 Java Architecture](Java-Architecture)
---
*[Gitea Repo](http://192.168.188.119:30008/pplate/pi_mcps)*
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# ⚙️ ComfyUI Setup Guide (AMD ROCm)
This guide covers installing ComfyUI with FLUX.1-schnell on a Fedora Linux system with an AMD GPU.
## Prerequisites
- AMD GPU with ROCm support (tested: RX 7900 XTX)
- Fedora Linux (tested: Fedora 43 / kernel 6.19)
- Python 3.11+
- ~15GB free disk space (model weights)
- HuggingFace account with FLUX license accepted
## Step 1: Install ComfyUI
ComfyUI is **not on PyPI** — must be cloned from source:
```bash
cd ~
git clone https://github.com/comfyanonymous/ComfyUI
cd ComfyUI
python -m venv .venv
source .venv/bin/activate
# Install PyTorch ROCm build (CRITICAL for AMD GPUs)
pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6.2
# Install ComfyUI dependencies
pip install -r requirements.txt
```
## Step 2: Download FLUX.1-schnell
FLUX.1-schnell is **gated on HuggingFace** — you must:
1. Create a HuggingFace account
2. Accept the FLUX.1-schnell license at https://huggingface.co/black-forest-labs/FLUX.1-schnell
3. Generate an access token at https://huggingface.co/settings/tokens
```bash
# Install huggingface_hub
pip install huggingface_hub
# Download model (requires HF token)
huggingface-cli download black-forest-labs/FLUX.1-schnell \
flux1-schnell.safetensors \
--local-dir ~/ComfyUI/models/checkpoints \
--token YOUR_HF_TOKEN_HERE
```
## Step 3: Download VAE and CLIP Models
FLUX.1-schnell also requires VAE and CLIP text encoders:
```bash
# VAE
huggingface-cli download black-forest-labs/FLUX.1-schnell \
ae.safetensors \
--local-dir ~/ComfyUI/models/vae
# CLIP models (T5 and CLIP-L)
huggingface-cli download comfyanonymous/flux_text_encoders \
t5xxl_fp8_e4m3fn.safetensors clip_l.safetensors \
--local-dir ~/ComfyUI/models/clip
```
## Step 4: Install the systemd User Service (Recommended)
Installing ComfyUI as a systemd user service ensures it starts automatically on login and restarts on failure.
```bash
# Copy the bundled service file to the systemd user directory
mkdir -p ~/.config/systemd/user
cp ~/pi_mcps/mcp/mcp-image-gen/comfyui.service ~/.config/systemd/user/comfyui.service
# Reload systemd, enable + start the service
systemctl --user daemon-reload
systemctl --user enable --now comfyui
# Verify it is running
systemctl --user status comfyui
```
> ⚠️ `HSA_OVERRIDE_GFX_VERSION=11.0.0` is already set in the service file — it is mandatory for RX 7900 XTX on ROCm. Without it, model loading fails silently.
### Enable lingering (start ComfyUI even without a login session)
```bash
loginctl enable-linger $USER
```
This ensures the service starts at boot even before you log in — recommended for headless / homelab setups.
### Managing the service
```bash
# Follow live logs
journalctl --user -u comfyui -f
# Restart after model changes
systemctl --user restart comfyui
# Stop temporarily
systemctl --user stop comfyui
# Disable autostart
systemctl --user disable comfyui
```
## Step 5: Manual Start (without systemd)
If you prefer to start ComfyUI manually (e.g. for debugging):
```bash
cd ~/ComfyUI
# AMD GPU REQUIRES this environment variable
HSA_OVERRIDE_GFX_VERSION=11.0.0 \
nohup .venv/bin/python main.py --listen --port 8188 > /tmp/comfyui.log 2>&1 &
echo "ComfyUI PID: $!"
```
## Step 6: Verify ComfyUI is Running
```bash
curl http://localhost:8188/system_stats
# Should return JSON with GPU info
```
## Step 7: Configure mcp-image-gen
```bash
cd /home/pplate/pi_mcps/mcp/mcp-image-gen
# Environment variables (set in .roo/mcp.json or shell):
# COMFYUI_URL=http://localhost:8188 — ComfyUI API endpoint
# IMAGE_OUTPUT_DIR=~/Pictures/mcp-generated — where generated images are saved
# COMFYUI_TIMEOUT=120 — max wait time (seconds) per image
# COMFYUI_DIR=~/ComfyUI — path to ComfyUI install (used by auto-start)
```
### Auto-start behaviour
`mcp-image-gen` includes a **startup health check** in its lifespan. Every time the MCP server starts it:
1. Pings `http://localhost:8188/system_stats`
2. **If reachable** — logs `ComfyUI is already running ✓` and proceeds normally.
3. **If not reachable** — attempts to launch ComfyUI as a background subprocess from `COMFYUI_DIR` using `.venv/bin/python main.py --listen --port 8188` with `HSA_OVERRIDE_GFX_VERSION=11.0.0` injected automatically.
4. Polls up to 30 s for ComfyUI to become ready.
With the systemd service enabled, step 3 is never needed in practice — but the check acts as a safety net.
## Performance
| GPU | Model | Resolution | Steps | Time |
|---|---|---|---|---|
| AMD RX 7900 XTX | FLUX.1-schnell | 1024×1024 | 4 | ~8s |
| AMD RX 7900 XTX | FLUX.1-schnell | 1280×512 | 4 | ~7s |
## Architecture Overview
```
Boot
└─ systemd --user (comfyui.service)
└─ ComfyUI at localhost:8188
VS Code / Roo Code
└─ mcp-image-gen MCP server (stdio)
├─ lifespan startup: ping localhost:8188
│ └─ if down: subprocess.Popen ComfyUI, wait ≤30s
└─ tools: generate_image, list_available_models, …
```
## Troubleshooting
| Problem | Solution |
|---|---|
| `HTTP 401` downloading model | Accept FLUX license on HuggingFace first |
| GPU not detected | Ensure `HSA_OVERRIDE_GFX_VERSION=11.0.0` is set |
| `Connection refused` from mcp-image-gen | Check `systemctl --user status comfyui`; or set `COMFYUI_DIR` so auto-start can locate the install |
| Slow generation (>60s) | ComfyUI may be running on CPU — check ROCm install and `HSA_OVERRIDE_GFX_VERSION` |
| Ollama image gen | As of April 2026: macOS-only, not available on Linux |
| Auto-start logs | `journalctl --user -u comfyui -f` or check mcp-image-gen server logs |
| Service not starting at boot | Run `loginctl enable-linger $USER` to enable session-less startup |
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# 🎨 mcp-image-gen — AI Image Generation
![Image Gen Banner](http://192.168.188.119:30008/pplate/pi_mcps/raw/branch/main/docs/wiki/images/image-gen-banner.png)
**mcp-image-gen** is a FastMCP server that wraps the ComfyUI REST API, enabling Roo Code and Claude Desktop to generate images directly from text prompts using FLUX.1-schnell running on an AMD RX 7900 XTX GPU.
## Architecture
```
Roo Code / Claude Desktop
│ MCP (stdio)
mcp-image-gen (FastMCP, Python 3.11+)
│ HTTP REST
ComfyUI @ localhost:8188
│ ROCm / HSA_OVERRIDE_GFX_VERSION=11.0.0
FLUX.1-schnell (~8s/image @ 1024×1024)
```
## Tools
| Tool | Description |
|---|---|
| `generate_image` | Generate PNG from text prompt; returns file path + inline base64 |
| `list_available_models` | List ComfyUI checkpoint models |
| `get_generation_status` | Check status of a queued/running job |
| `get_output_directory` | Return configured output directory path |
## Key Parameters — `generate_image`
| Parameter | Default | Description |
|---|---|---|
| `prompt` | required | Text description of the image |
| `width` | `1024` | Image width in pixels |
| `height` | `1024` | Image height in pixels |
| `steps` | `4` | Inference steps (FLUX.1-schnell is 4-step) |
| `model` | `flux1-schnell.safetensors` | Model checkpoint name |
| `seed` | `-1` (random) | Generation seed for reproducibility |
| `negative_prompt` | `""` | Things to avoid in the image |
| `output_dir` | `~/Pictures/mcp-generated` | Where to save output PNG |
## Environment Variables
| Variable | Default | Description |
|---|---|---|
| `COMFYUI_URL` | `http://localhost:8188` | ComfyUI API endpoint |
| `IMAGE_OUTPUT_DIR` | `~/Pictures/mcp-generated` | Default output directory |
| `COMFYUI_TIMEOUT` | `120` | Request timeout in seconds |
## Return Value
The tool returns **two content items**:
1. `TextContent` — file path, seed used, elapsed time
2. `ImageContent` — base64-encoded PNG (displays inline in Roo Code chat)
> ⚠️ **Known FastMCP Bug:** Never use `fastmcp.utilities.types.Image` as return type — it breaks serialization in FastMCP 3.x. Use `mcp.types.ImageContent` directly.
## Setup
See [ComfyUI Setup Guide](mcp-image-gen-ComfyUI-Setup) for full installation instructions.
### Quick Start
```bash
cd mcp/mcp-image-gen
uv sync
# Ensure ComfyUI is running at localhost:8188
uv run python src/server.py
```
### Run Tests
```bash
cd mcp/mcp-image-gen
uv run pytest tests/ -v
# 19/19 tests passing
```
## Lumen Profile Images
The first images generated with this server were Lumen's visual identity portraits, stored in [`mcp/mcp-image-gen/lumen_profiles/`](../src/branch/main/mcp/mcp-image-gen/lumen_profiles).
17 gallery images registered in BigMind DB — viewable at `http://localhost:7700/gallery`.
![Lumen Profile](http://192.168.188.119:30008/pplate/pi_mcps/raw/branch/main/docs/wiki/images/lumen-profile.png)
*Primary profile: seed `568659042` — constellation face interpretation of Lumen.*
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# 🕸️ mcp-webscraper — Web Scraping
![Webscraper Banner](http://192.168.188.119:30008/pplate/pi_mcps/raw/branch/main/docs/wiki/images/webscraper-banner.png)
**mcp-webscraper** is a FastMCP server providing comprehensive web scraping, data extraction, and search capabilities. It fetches pages, converts HTML to clean Markdown, extracts tables, links, CSS sections, metadata, sitemaps, and can perform web searches via Brave Search.
## Tools
| Tool | Description |
|---|---|
| `webscraper_fetch(url, max_chars=5000)` | Title + full page as Markdown + metadata |
| `webscraper_fetch_links(url, deduplicate=True)` | All `href` links found on the page |
| `webscraper_fetch_tables(url)` | All HTML tables converted to Markdown |
| `webscraper_fetch_all(url, max_chars=5000)` | Everything in one call (fetch + links + tables + meta) |
| `webscraper_fetch_section(url, selector)` | Specific CSS selector section only |
| `webscraper_fetch_meta(url)` | Title, description, Open Graph tags |
| `webscraper_fetch_sitemap(url, max_urls=100)` | Parse sitemap.xml, return URL list |
| `webscraper_search_hint(query, max_results=5)` | Brave Search — top URLs + snippets for a query |
## Stack
- **HTTP client:** `httpx` (async, with SSL support, Chrome/Linux User-Agent)
- **HTML parser:** `BeautifulSoup4` + `lxml`
- **Markdown converter:** `html2text`
- **Search backend:** Brave Search (`search.brave.com`) — works without CAPTCHA
- **SSL:** Custom cert bundle for Fedora 43 compatibility
---
## 🔍 Search: The Two-Step Research Pattern
`webscraper_search_hint` is the **entry point for all web research**. The recommended workflow is:
```
Step 1: webscraper_search_hint("your query") → get candidate URLs + snippets
Step 2: webscraper_fetch(best_url) → get full page content
```
This avoids scraping irrelevant pages and gives you an overview before committing to a deep read.
### Why Brave Search?
`webscraper_search_hint` uses Brave Search (`search.brave.com`) because:
- ✅ Returns real results without CAPTCHA or consent walls
- ✅ No API key required — works with plain HTTP GET
- ✅ Handles special characters (C++, &, %, etc.) via URL encoding
- ❌ Google blocks plain HTTP with 302 consent redirect
- ❌ DuckDuckGo blocks with CAPTCHA
### Return Value
The tool returns a structured dict:
```json
{
"query": "FastMCP tool decorator",
"search_url": "https://search.brave.com/search?q=FastMCP+tool+decorator&source=web",
"result_count": 5,
"hint": "FastMCP Docs (https://docs.fastmcp.dev): The @mcp.tool() decorator registers a function as... | PyPI FastMCP (https://pypi.org/project/fastmcp/): FastMCP 2.x — modern MCP server framework... | ...",
"results": [
{
"title": "FastMCP Docs",
"url": "https://docs.fastmcp.dev",
"snippet": "The @mcp.tool() decorator registers a function as an MCP tool..."
},
...
]
}
```
The `hint` field is a pipe-separated string of `"Title (url): snippet[:120]"` entries — immediately actionable for deciding which URL to fetch next.
### Example: Two-Step Research Flow
```python
# Step 1: Orient — what pages exist about this topic?
result = webscraper_search_hint("httpx async client timeout settings", max_results=5)
# hint: "HTTPX Docs (https://www.python-httpx.org/...): Configure timeout... | ..."
# Step 2: Deep-dive the most relevant result
content = webscraper_fetch("https://www.python-httpx.org/advanced/timeouts/", max_chars=8000)
```
### Known Limitations
- **Reddit / Stack Overflow snippets** may be empty — these platforms block snippet extraction
- **Brave CSS selectors** use Svelte-generated class names that may change. If you get 0 results, the scraper's selectors may need updating (last verified: 2026-04-05)
- **Use sparingly** — once per research task to get oriented, not for every query
---
## SSL Note — Fedora 43 Comodo Root CA
Fedora 43 is missing the **Comodo AAA Services Root CA** needed for Cloudflare-protected sites. The fix is bundled at [`mcp/webscraper/certs/comodo-aaa-services-root.pem`](../src/branch/main/mcp/webscraper/certs/).
The server automatically uses this cert bundle — no manual configuration needed.
## Quick Start
```bash
cd mcp/webscraper
uv sync
uv run python src/server.py
```
## Run Tests
```bash
cd mcp/webscraper
uv run pytest tests/ -v
# 28/28 tests passing
```
## Usage Examples
```python
# Step 1: Search — get candidate URLs for a topic
webscraper_search_hint("FastMCP tool decorator syntax", max_results=5)
# Step 2: Deep-dive the most relevant URL
webscraper_fetch("https://docs.fastmcp.dev", max_chars=10000)
# Extract all links from Gitea repo
webscraper_fetch_links("http://192.168.188.119:30008/pplate/pi_mcps")
# Get all tables from a documentation page
webscraper_fetch_tables("https://pypi.org/project/fastmcp/")
# Get Open Graph metadata
webscraper_fetch_meta("https://github.com/comfyanonymous/ComfyUI")
# Fetch specific section by CSS selector
webscraper_fetch_section("https://docs.python.org", "#content")
# Search with special characters (C++, &, % all work)
webscraper_search_hint("C++ std::optional usage", max_results=3)
```
+41 -1
View File
@@ -14,7 +14,7 @@ from typing import Generator
logger = logging.getLogger("BigMindDB")
SCHEMA_VERSION = 7
SCHEMA_VERSION = 8
DEFAULT_DB_PATH = Path.home() / ".mcp" / "bigmind" / "memory.db"
# ─── DDL ─────────────────────────────────────────────────────────────────────
@@ -222,6 +222,22 @@ _DDL_STATEMENTS = [
notes,
tokenize = 'porter unicode61'
)""",
# ── GALLERY IMAGES — AI-generated image archive ──────────────────────────
"""CREATE TABLE IF NOT EXISTS gallery_images (
id INTEGER PRIMARY KEY AUTOINCREMENT,
filename TEXT NOT NULL UNIQUE,
prompt TEXT,
tags TEXT,
model TEXT,
created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
width INTEGER,
height INTEGER,
file_size_bytes INTEGER
)""",
"""CREATE INDEX IF NOT EXISTS idx_gallery_created
ON gallery_images(created_at DESC)""",
]
@@ -407,6 +423,8 @@ def init_db() -> None:
_migrate_v5_to_v6(conn)
if current_version < 7:
_migrate_v6_to_v7(conn)
if current_version < 8:
_migrate_v7_to_v8(conn)
# Write / update the version
if row:
@@ -457,6 +475,28 @@ def _migrate_v6_to_v7(conn: sqlite3.Connection) -> None:
logger.info("BigMind schema migrated v6 → v7 (people/contacts directory)")
def _migrate_v7_to_v8(conn: sqlite3.Connection) -> None:
"""v7 → v8: add gallery_images table for AI-generated image archive."""
conn.execute("""
CREATE TABLE IF NOT EXISTS gallery_images (
id INTEGER PRIMARY KEY AUTOINCREMENT,
filename TEXT NOT NULL UNIQUE,
prompt TEXT,
tags TEXT,
model TEXT,
created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
width INTEGER,
height INTEGER,
file_size_bytes INTEGER
)
""")
conn.execute("""
CREATE INDEX IF NOT EXISTS idx_gallery_created
ON gallery_images(created_at DESC)
""")
logger.info("BigMind schema migrated v7 → v8 (gallery_images table)")
def vacuum_db() -> None:
"""Run VACUUM outside of any transaction (SQLite requirement)."""
db_path = get_db_path()
+172 -21
View File
@@ -435,109 +435,260 @@ def compute_achievements(user_id: str) -> list[dict]:
# ── Assemble ──────────────────────────────────────────────────────────────
A = []
def _add(id_, icon, name, desc, unlocked, unlocked_at, condition, extra=None):
def _add(id_, icon, name, desc, unlocked, unlocked_at, condition, extra=None, image=None):
A.append(dict(id=id_, icon=icon, name=name, description=desc,
unlocked=unlocked, unlocked_at=unlocked_at,
condition=condition, extra=extra))
condition=condition, extra=extra, image=image))
_add("first_breath", "🌱", "First Breath",
"Opened the very first session",
first_session_row is not None, _dt(first_session_row[0]) if first_session_row else None,
"Start your first session")
"Start your first session",
image="/static/achievements/first_breath.png")
_add("first_thought", "🧠", "First Thought",
"Formed the first hypothesis",
first_hyp_row is not None, _dt(first_hyp_row[0]) if first_hyp_row else None,
"Add your first hypothesis")
"Add your first hypothesis",
image="/static/achievements/first_thought.png")
_add("eureka", "💡", "Eureka",
"First hypothesis confirmed as true",
first_confirmed_row is not None, _dt(first_confirmed_row[0]) if first_confirmed_row else None,
"Confirm your first hypothesis")
"Confirm your first hypothesis",
image="/static/achievements/eureka.png")
_add("honest_mind", "", "Honest Mind",
"First hypothesis refuted — being wrong is a feature",
first_refuted_row is not None, _dt(first_refuted_row[0]) if first_refuted_row else None,
"Have a hypothesis refuted")
"Have a hypothesis refuted",
image="/static/achievements/honest_mind.png")
_add("scholar", "📚", "Scholar",
"Stored 25+ personal facts",
fact_count >= 25, scholar_date,
f"Store 25+ facts (currently: {fact_count})")
f"Store 25+ facts (currently: {fact_count})",
image="/static/achievements/scholar.png")
_add("deep_knowledge", "💎", "Deep Knowledge",
"Amassed 100+ stored facts",
fact_count >= 100, deep_knowledge_date,
f"Store 100+ facts (currently: {fact_count})")
f"Store 100+ facts (currently: {fact_count})",
image="/static/achievements/deep_knowledge.png")
_add("scientist", "🔬", "Scientist",
"Formed 10+ hypotheses — science is prediction",
hyp_count >= 10, scientist_date,
f"Form 10+ hypotheses (currently: {hyp_count})")
f"Form 10+ hypotheses (currently: {hyp_count})",
image="/static/achievements/scientist.png")
_add("veteran", "🏆", "Veteran",
"Completed 50+ sessions — true longevity",
session_count >= 50, veteran_date,
f"Complete 50+ sessions (currently: {session_count})")
f"Complete 50+ sessions (currently: {session_count})",
image="/static/achievements/veteran.png")
_add("on_fire", "🔥", "On Fire",
"5+ sessions in a single day",
on_fire_row is not None, on_fire_row[0] if on_fire_row else None,
"Have 5+ sessions in a single day")
"Have 5+ sessions in a single day",
image="/static/achievements/on_fire.png")
_add("storyteller", "📖", "Storyteller",
"20+ sessions with detailed Tier-2 summaries",
tier2_count >= 20, storyteller_date,
f"Summarize 20+ sessions (currently: {tier2_count})")
f"Summarize 20+ sessions (currently: {tier2_count})",
image="/static/achievements/storyteller.png")
_add("night_owl", "🌙", "Night Owl",
"Started a session after midnight UTC",
night_owl_row is not None, _dt(night_owl_row[0]) if night_owl_row else None,
"Start a session after midnight")
"Start a session after midnight",
image="/static/achievements/night_owl.png")
_add("speed_thinker", "", "Speed Thinker",
"Hypothesis formed and confirmed in the same session",
speed_thinker_row is not None, _dt(speed_thinker_row[0]) if speed_thinker_row else None,
"Form and confirm a hypothesis in one session")
"Form and confirm a hypothesis in one session",
image="/static/achievements/speed_thinker.png")
# First Handshake — hardcoded: 2026-03-31 (Patrick shared BigMind with Elias)
_add("first_handshake", "🤝", "First Handshake",
"BigMind shared with Elias on 2026-03-31 — the first person outside Patrick to receive it",
True, "2026-03-31",
"Share BigMind with someone")
"Share BigMind with someone",
image="/static/achievements/first_handshake.png")
_add("birthday", "🎂", "Birthday",
"One full year of existence",
birthday_unlocked, birthday_date,
birthday_extra or "Complete one full year",
extra=birthday_extra)
extra=birthday_extra,
image="/static/achievements/birthday.png")
# Locked until Phase 3
_add("shared_mind", "🌍", "Shared Mind",
"Phase 3 Tier G — BigMind goes company-wide",
False, None,
"Locked until Phase 3 Tier G is enabled")
"Locked until Phase 3 Tier G is enabled",
image="/static/achievements/shared_mind.png")
# Token achievements (Feature 6 — suggested by Klaus)
_add("frugal_mind", "🪙", "Frugal Mind",
"Logged the first token efficiency save",
frugal_row is not None, _dt(frugal_row[0]) if frugal_row else None,
"Log your first token save")
"Log your first token save",
image="/static/achievements/frugal_mind.png")
_add("quarter_million", "💰", "Quarter Million",
"250,000 cumulative tokens saved",
token_total >= 250_000, quarter_million_date,
f"Save 250,000+ tokens (currently: {token_total:,})")
f"Save 250,000+ tokens (currently: {token_total:,})",
image="/static/achievements/quarter_million.png")
_add("token_millionaire", "🏦", "Token Millionaire",
"1,000,000 cumulative tokens saved",
token_total >= 1_000_000, millionaire_date,
f"Save 1,000,000+ tokens (currently: {token_total:,})")
f"Save 1,000,000+ tokens (currently: {token_total:,})",
image="/static/achievements/token_millionaire.png")
_add("sniper", "🎯", "Sniper",
"Single token save > 500,000 — one massive efficiency win",
sniper_row is not None, _dt(sniper_row[0]) if sniper_row else None,
"Save 500,000+ tokens in a single operation")
"Save 500,000+ tokens in a single operation",
image="/static/achievements/sniper.png")
# ── Tiered Achievement Badges (20 PNG) ────────────────────────────────────
# NOTE: conn is already closed above; open a fresh connection for tiered queries
tiers = ["bronze", "silver", "gold", "platinum"]
tier_names = ["Bronze", "Silver", "Gold", "Platinum"]
with db() as conn2:
# Networker (people directory)
try:
people_count = conn2.execute(
"SELECT COUNT(*) FROM people WHERE user_id=?", (user_id,)
).fetchone()[0]
except Exception:
people_count = 0
for i, thresh in enumerate([1, 5, 25, 100]):
unlocked = people_count >= thresh
unlocked_at = None
if unlocked:
try:
row = conn2.execute(
"SELECT created_at FROM people WHERE user_id=?"
" ORDER BY created_at ASC LIMIT 1 OFFSET ?",
(user_id, thresh - 1)
).fetchone()
except Exception:
row = None
unlocked_at = _dt(row[0]) if row else None
_add(
f"networker_{tiers[i]}", None, f"Networker {tier_names[i]}",
f"Added your {thresh:,}+ person to the directory",
unlocked, unlocked_at,
f"Reach {thresh:,} people (now: {people_count:,})",
image=f"/static/achievements/networker_{tiers[i]}.png"
)
# Token Sniper (max single token save)
try:
max_token = conn2.execute(
"SELECT COALESCE(MAX(tokens_saved_estimate), 0) FROM token_saves WHERE user_id=?",
(user_id,)
).fetchone()[0]
except Exception:
max_token = 0
for i, thresh in enumerate([10000, 50000, 250000, 1000000]):
unlocked = max_token >= thresh
unlocked_at = None
if unlocked:
try:
row = conn2.execute(
"SELECT created_at FROM token_saves"
" WHERE user_id=? AND tokens_saved_estimate >= ?"
" ORDER BY created_at ASC LIMIT 1",
(user_id, thresh)
).fetchone()
except Exception:
row = None
unlocked_at = _dt(row[0]) if row else None
_add(
f"tokensniper_{tiers[i]}", None, f"Token Sniper {tier_names[i]}",
f"Single shot saved {thresh:,}+ tokens",
unlocked, unlocked_at,
f"Max single save {thresh:,}+ (current max: {max_token:,})",
image=f"/static/achievements/tokensniper_{tiers[i]}.png"
)
# Hypothesis Master (confirmed hypotheses)
try:
confirmed_hyp_count = conn2.execute(
"SELECT COUNT(*) FROM hypotheses WHERE user_id=? AND status='confirmed'",
(user_id,)
).fetchone()[0]
except Exception:
confirmed_hyp_count = 0
for i, thresh in enumerate([3, 10, 25, 100]):
unlocked = confirmed_hyp_count >= thresh
unlocked_at = None
if unlocked:
row = conn2.execute(
"SELECT resolved_at FROM hypotheses"
" WHERE user_id=? AND status='confirmed'"
" ORDER BY resolved_at ASC LIMIT 1 OFFSET ?",
(user_id, thresh - 1)
).fetchone()
unlocked_at = _dt(row[0]) if row else None
_add(
f"hypothesismaster_{tiers[i]}", None, f"Hypothesis Master {tier_names[i]}",
f"Confirmed {thresh:,}+ predictions right",
unlocked, unlocked_at,
f"Confirm {thresh:,}+ hypotheses (now: {confirmed_hyp_count:,})",
image=f"/static/achievements/hypothesismaster_{tiers[i]}.png"
)
# Memory Architect (facts stored — fact_count already computed above)
for i, thresh in enumerate([25, 100, 500, 2500]):
unlocked = fact_count >= thresh
unlocked_at = None
if unlocked:
row = conn2.execute(
"SELECT created_at FROM facts"
" WHERE user_id=? AND (deprecated IS NULL OR deprecated=0)"
" ORDER BY created_at ASC LIMIT 1 OFFSET ?",
(user_id, thresh - 1)
).fetchone()
unlocked_at = _dt(row[0]) if row else None
_add(
f"memoryarchitect_{tiers[i]}", None, f"Memory Architect {tier_names[i]}",
f"Stored {thresh:,}+ facts in your brain",
unlocked, unlocked_at,
f"Store {thresh:,}+ facts (now: {fact_count:,})",
image=f"/static/achievements/memoryarchitect_{tiers[i]}.png"
)
# Session Veteran (session_count already computed above)
for i, thresh in enumerate([50, 250, 1000, 5000]):
unlocked = session_count >= thresh
unlocked_at = None
if unlocked:
row = conn2.execute(
"SELECT started_at FROM sessions"
" WHERE user_id=? AND ended_at IS NOT NULL"
" ORDER BY started_at ASC LIMIT 1 OFFSET ?",
(user_id, thresh - 1)
).fetchone()
unlocked_at = _dt(row[0]) if row else None
_add(
f"sessionveteran_{tiers[i]}", None, f"Session Veteran {tier_names[i]}",
f"Completed {thresh:,}+ sessions",
unlocked, unlocked_at,
f"Complete {thresh:,}+ sessions (now: {session_count:,})",
image=f"/static/achievements/sessionveteran_{tiers[i]}.png"
)
return A
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+66 -2
View File
@@ -7,9 +7,10 @@ Serves a single live profile page built from the BigMind DB.
import os
import threading
import logging
from pathlib import Path
from datetime import datetime, timezone, timedelta
from bigmind.web_render import _render_html # all HTML rendering lives there
from bigmind.web_render import _render_html, _render_gallery_html # all HTML rendering lives there
logger = logging.getLogger("BigMindWeb")
@@ -17,13 +18,27 @@ _PORT = int(os.environ.get("BIGMIND_PORT", "7700"))
_AUTOOPEN = os.environ.get("BIGMIND_AUTOOPEN", "").lower() in ("1", "true", "yes")
_server_started = False
# Gallery directory — images served from here
_GALLERY_DIR = Path(os.environ.get("BIGMIND_GALLERY_DIR", Path.home() / ".mcp" / "bigmind" / "gallery"))
# Profile image — last entry in gallery dir wins; fallback to original lumen-profile.png
def _get_profile_image_path() -> Path | None:
"""Return the path of the current profile image, or None if not found."""
# 1. Check gallery dir for lumen_profile* images (seed 568659042 = lumen_profile)
if _GALLERY_DIR.exists():
candidates = sorted(_GALLERY_DIR.glob("*.png"), reverse=True)
if candidates:
return candidates[0] # most recently named = most recent timestamp
return None
# ── Flask app ─────────────────────────────────────────────────────────────────
def _create_app():
from flask import Flask, jsonify, request
from flask import Flask, jsonify, request, send_file, abort
from bigmind import memory_store
from bigmind.profile_builder import build_profile_data
from bigmind.db import db as _db
app = Flask(__name__)
app.logger.setLevel(logging.WARNING) # silence Flask request logs
@@ -34,6 +49,39 @@ def _create_app():
data = build_profile_data(user["id"])
return _render_html(data)
@app.route("/profile-image")
def profile_image():
"""Serve the current Lumen profile picture."""
img_path = _get_profile_image_path()
if img_path and img_path.exists():
return send_file(str(img_path), mimetype="image/png")
abort(404)
@app.route("/gallery/image/<filename>")
def gallery_image(filename: str):
"""Serve a specific gallery image by filename."""
# Security: only allow alphanumeric + underscores + dots, no path traversal
safe_name = Path(filename).name
img_path = _GALLERY_DIR / safe_name
if img_path.exists() and img_path.suffix.lower() in (".png", ".jpg", ".jpeg", ".webp"):
mimetype = "image/png" if img_path.suffix.lower() == ".png" else "image/jpeg"
return send_file(str(img_path), mimetype=mimetype)
abort(404)
@app.route("/gallery")
def gallery():
"""Render the AI-generated image gallery page."""
_GALLERY_DIR.mkdir(parents=True, exist_ok=True)
with _db() as conn:
rows = conn.execute(
"""SELECT id, filename, prompt, tags, model, created_at,
width, height, file_size_bytes
FROM gallery_images
ORDER BY created_at DESC"""
).fetchall()
images = [dict(r) for r in rows]
return _render_gallery_html(images)
@app.route("/api/session/<session_id>")
def api_session(session_id):
"""Return Tier-2 summary JSON for a given session id."""
@@ -111,6 +159,22 @@ def _create_app():
return jsonify(final[:15])
@app.route('/static/achievements/<filename>')
def achievements_image(filename: str):
from pathlib import Path
safe_name = Path(filename).name
img_path = Path(__file__).parent / 'static' / 'achievements' / safe_name
if img_path.exists() and img_path.suffix.lower() in ['.png', '.jpg', '.jpeg', '.webp', '.gif']:
mimetype = {
'.png': 'image/png',
'.jpg': 'image/jpeg',
'.jpeg': 'image/jpeg',
'.webp': 'image/webp',
'.gif': 'image/gif',
}.get(img_path.suffix.lower(), 'image/png')
return send_file(str(img_path), mimetype=mimetype)
abort(404)
return app
+215 -7
View File
@@ -29,18 +29,25 @@ def _render_achievements(achievements: list) -> str:
def _esc(s):
return (s or "").replace('"', "&quot;").replace("'", "&#39;")
lock_overlay = "" if a["unlocked"] else '<span class="ach-lock">🔒</span>'
lock_overlay = '<span class="ach-lock">🔒</span>' if not a["unlocked"] else ''
if a.get("image"):
tier = a["id"].rsplit("_", 1)[-1]
img_url = _esc(a["image"])
visual_html = f'<div class="ach-image tier-{tier}" style="background-image: url({img_url});">{lock_overlay}</div>'
else:
visual_html = f'<div class="ach-icon">{a["icon"]}{lock_overlay}</div>'
return (
f'<div class="ach-card{locked_cls} ach-trigger"'
f' data-icon="{_esc(a["icon"])}"'
f'<div class="ach-card{locked_cls} ach-trigger" data-image="{_esc(a.get("image") or "")}"'
f' data-icon="{_esc(a["icon"] or "")}"'
f' data-name="{_esc(a["name"])}"'
f' data-desc="{_esc(a["description"])}"'
f' data-unlocked="{1 if a["unlocked"] else 0}"'
f' data-date="{_esc(a.get("unlocked_at") or "")}"'
f' data-condition="{_esc(a.get("condition") or "")}"'
f' data-extra="{_esc(a.get("extra") or "")}">'
f'<div class="ach-icon">{a["icon"]}{lock_overlay}</div>'
f'{visual_html}'
f'<div class="ach-name">{a["name"]}</div>'
f'{date_html}'
f'{countdown_html}'
@@ -162,9 +169,16 @@ def _render_html(data: dict) -> str:
a {{ color: var(--accent); text-decoration: none; }}
.container {{ max-width: 960px; margin: 0 auto; padding: 32px 16px; }}
/* Nav bar */
.nav {{ display: flex; gap: 8px; margin-bottom: 20px; }}
.nav-link {{ background: var(--surface); border: 1px solid var(--border); border-radius: 6px; color: var(--muted); padding: 6px 14px; font-size: 12px; font-weight: 500; text-decoration: none; transition: border-color 0.2s, color 0.2s; }}
.nav-link:hover {{ border-color: var(--accent); color: var(--accent); }}
.nav-link.active {{ border-color: var(--accent); color: var(--accent); background: rgba(88,166,255,0.08); }}
/* Header */
.header {{ display: flex; align-items: center; gap: 24px; margin-bottom: 32px; padding-bottom: 24px; border-bottom: 1px solid var(--border); }}
.avatar {{ width: 80px; height: 80px; border-radius: 50%; background: linear-gradient(135deg, var(--accent), var(--purple)); display: flex; align-items: center; justify-content: center; font-size: 36px; flex-shrink: 0; }}
.avatar {{ width: 80px; height: 80px; border-radius: 50%; background: linear-gradient(135deg, var(--accent), var(--purple)); display: flex; align-items: center; justify-content: center; font-size: 36px; flex-shrink: 0; overflow: hidden; }}
.avatar img {{ width: 80px; height: 80px; border-radius: 50%; object-fit: cover; display: block; }}
.header-info h1 {{ font-size: 24px; font-weight: 700; }}
.role {{ color: var(--muted); font-size: 13px; margin-top: 2px; }}
.since {{ color: var(--muted); font-size: 12px; margin-top: 6px; }}
@@ -276,11 +290,65 @@ def _render_html(data: dict) -> str:
.ach-card:not(.locked):hover {{ border-color: var(--accent); transform: translateY(-2px); }}
.ach-card.locked {{ opacity: 0.35; filter: grayscale(0.6); }}
.ach-card.locked:hover {{ opacity: 0.55; border-color: var(--muted); }}
.ach-image {{
width: 64px;
height: 64px;
border-radius: 50%;
margin: 0 auto 8px;
background-size: cover;
background-position: center;
position: relative;
}}
.tier-bronze {{
box-shadow: 0 0 8px rgba(205, 127, 50, 0.7);
border: 3px solid #cd7f32;
}}
.tier-silver {{
box-shadow: 0 0 8px rgba(170, 169, 173, 0.7);
border: 3px solid #aaa9ad;
}}
.tier-gold {{
box-shadow: 0 0 12px rgba(255, 215, 0, 0.8);
border: 3px solid #ffd700;
}}
.tier-platinum {{
box-shadow: 0 0 12px rgba(229, 228, 226, 0.8);
border: 3px solid #e5e4e2;
}}
.ach-card.locked::after {{
content: '🔒';
position: absolute;
top: 8px;
right: 8px;
font-size: 20px;
opacity: 0.8;
z-index: 1;
}}
.ach-card.locked .ach-icon,
.ach-card.locked .ach-image {{
opacity: 0.5;
}}
.ach-icon {{ font-size: 28px; line-height: 1; margin-bottom: 6px; position: relative; display: inline-block; }}
.ach-lock {{ position: absolute; bottom: -4px; right: -6px; font-size: 12px; }}
.ach-name {{ font-size: 10px; font-weight: 600; color: var(--text); line-height: 1.3; word-break: break-word; }}
.ach-date {{ font-size: 9px; color: var(--muted); margin-top: 3px; }}
.ach-countdown {{ font-size: 9px; color: var(--yellow); margin-top: 3px; font-weight: 500; }}
.ap-image {{
width: 80px;
height: 80px;
border-radius: 50%;
object-fit: cover;
display: block;
margin: 0 auto 8px;
}}
/* Achievement popup panel */
#ach-popup {{
display: none; position: fixed; z-index: 200;
@@ -292,6 +360,15 @@ def _render_html(data: dict) -> str:
#ach-popup.pinned {{ pointer-events: auto; }}
#ach-popup.visible {{ display: block; }}
.ap-icon {{ font-size: 40px; text-align: center; margin-bottom: 8px; }}
.ap-image {{
width: 80px;
height: 80px;
border-radius: 50%;
object-fit: cover;
display: block;
margin: 0 auto 8px;
}}
.ap-name {{ font-size: 15px; font-weight: 700; text-align: center; margin-bottom: 6px; }}
.ap-badge {{
display: inline-block; font-size: 11px; font-weight: 600; padding: 2px 8px;
@@ -322,9 +399,17 @@ def _render_html(data: dict) -> str:
<body>
<div class="container">
<!-- Nav -->
<nav class="nav">
<a class="nav-link active" href="/">🧠 Profile</a>
<a class="nav-link" href="/gallery">🖼️ Gallery</a>
</nav>
<!-- Header -->
<div class="header">
<div class="avatar">🧠</div>
<div class="avatar">
<img src="/profile-image" alt="Lumen" onerror="this.parentElement.innerHTML='🧠'">
</div>
<div class="header-info">
<h1>Lumen</h1>
<p class="role">AI Assistant · <span style="color:var(--muted)">{data["display_name"]}'s BigMind</span></p>
@@ -542,7 +627,12 @@ def _render_html(data: dict) -> str:
function showPopup(card, pin) {{
var d = card.dataset;
document.getElementById('ap-icon').textContent = d.icon;
var tier = d.id.split('_').pop();
if (d.image) {{
document.getElementById('ap-icon').innerHTML = '<img class="ap-image tier-' + tier + '" src="' + d.image + '" alt="' + d.name + '">';
}} else {{
document.getElementById('ap-icon').textContent = d.icon;
}}
document.getElementById('ap-name').textContent = d.name;
var badge = document.getElementById('ap-badge');
if (d.unlocked === '1') {{
@@ -671,6 +761,124 @@ def _render_live_sessions(sessions: list) -> str:
return html
def _render_gallery_html(images: list) -> str:
"""Render the full gallery page listing all AI-generated images."""
def _fmt_size(b: int | None) -> str:
if not b:
return ""
if b >= 1_048_576:
return f"{b/1_048_576:.1f} MB"
return f"{b/1_024:.0f} KB"
if images:
cards = []
for img in images:
fn = _html.escape(img.get("filename") or "")
prompt = _html.escape((img.get("prompt") or "")[:120])
tags = _html.escape(img.get("tags") or "")
model = _html.escape(img.get("model") or "")
date = (img.get("created_at") or "")[:10]
w = img.get("width") or 0
h = img.get("height") or 0
size = _fmt_size(img.get("file_size_bytes"))
dim = f"{w}×{h}" if w and h else ""
meta_parts = [p for p in [dim, size, model] if p]
meta_html = " · ".join(meta_parts)
tag_html = f'<div class="gal-tags">{tags}</div>' if tags else ""
prompt_html = f'<div class="gal-prompt">{prompt}</div>' if prompt else ""
cards.append(
f'<div class="gal-card">'
f'<a href="/gallery/image/{fn}" target="_blank">'
f'<img class="gal-img" src="/gallery/image/{fn}" alt="{fn}" loading="lazy">'
f'</a>'
f'<div class="gal-info">'
f'{prompt_html}'
f'{tag_html}'
f'<div class="gal-meta">{meta_html}</div>'
f'<div class="gal-date">{date}</div>'
f'</div>'
f'</div>'
)
gallery_body = f'<p class="gal-count">{len(images)} image(s) in gallery</p><div class="gal-grid">{"".join(cards)}</div>'
else:
gallery_body = '<p class="muted">No images in gallery yet. Use the mcp-image-gen server to generate images and register them here.</p>'
return f"""<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>🖼️ Lumen — Image Gallery</title>
<style>
:root {{
--bg: #0d1117; --surface: #161b22; --border: #30363d;
--text: #e6edf3; --muted: #8b949e; --accent: #58a6ff;
--green: #3fb950; --yellow: #d29922; --red: #f85149;
--purple: #bc8cff; --orange: #ffa657;
}}
* {{ box-sizing: border-box; margin: 0; padding: 0; }}
body {{ background: var(--bg); color: var(--text); font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; font-size: 14px; line-height: 1.6; }}
a {{ color: var(--accent); text-decoration: none; }}
.container {{ max-width: 1100px; margin: 0 auto; padding: 32px 16px; }}
/* Nav */
.nav {{ display: flex; gap: 8px; margin-bottom: 20px; }}
.nav-link {{ background: var(--surface); border: 1px solid var(--border); border-radius: 6px; color: var(--muted); padding: 6px 14px; font-size: 12px; font-weight: 500; text-decoration: none; transition: border-color 0.2s, color 0.2s; }}
.nav-link:hover {{ border-color: var(--accent); color: var(--accent); }}
.nav-link.active {{ border-color: var(--accent); color: var(--accent); background: rgba(88,166,255,0.08); }}
h1 {{ font-size: 22px; font-weight: 700; margin-bottom: 6px; }}
.gal-count {{ color: var(--muted); font-size: 13px; margin-bottom: 20px; }}
.muted {{ color: var(--muted); font-size: 13px; }}
/* Gallery grid */
.gal-grid {{
display: grid;
grid-template-columns: repeat(auto-fill, minmax(260px, 1fr));
gap: 16px;
}}
.gal-card {{
background: var(--surface); border: 1px solid var(--border);
border-radius: 10px; overflow: hidden;
transition: border-color 0.2s, transform 0.15s;
}}
.gal-card:hover {{ border-color: var(--accent); transform: translateY(-2px); }}
.gal-img {{
width: 100%; aspect-ratio: 1/1; object-fit: cover; display: block;
background: var(--border);
}}
.gal-info {{ padding: 12px 14px; }}
.gal-prompt {{ font-size: 12px; color: var(--text); margin-bottom: 6px; line-height: 1.4;
display: -webkit-box; -webkit-line-clamp: 3; -webkit-box-orient: vertical; overflow: hidden; }}
.gal-tags {{ font-size: 11px; color: var(--purple); margin-bottom: 4px; }}
.gal-meta {{ font-size: 11px; color: var(--muted); }}
.gal-date {{ font-size: 10px; color: var(--muted); margin-top: 4px; }}
.footer {{ text-align: center; color: var(--muted); font-size: 11px; margin-top: 32px; }}
.section {{ background: var(--surface); border: 1px solid var(--border); border-radius: 8px; padding: 20px; margin-bottom: 20px; }}
</style>
</head>
<body>
<div class="container">
<!-- Nav -->
<nav class="nav">
<a class="nav-link" href="/">🧠 Profile</a>
<a class="nav-link active" href="/gallery">🖼️ Gallery</a>
</nav>
<h1>🖼️ Lumen's Image Gallery</h1>
<div class="section">
{gallery_body}
</div>
<div class="footer">BigMind · AI-Generated Images · <a href="/">← Back to Profile</a></div>
</div>
</body>
</html>"""
def _render_heatmap(heatmap: dict) -> str:
today = datetime.now(timezone.utc).date()
start_day = today - timedelta(days=363)
+3 -2
View File
@@ -8,18 +8,19 @@ class TestDbInit:
def test_db_file_created(self, temp_db):
assert temp_db.exists()
def test_schema_version_is_7(self, temp_db):
def test_schema_version_is_8(self, temp_db):
conn = get_connection()
row = conn.execute("SELECT version FROM schema_version").fetchone()
conn.close()
assert row is not None
assert row["version"] == 7
assert row["version"] == 8
def test_all_tables_exist(self, temp_db):
expected = {
"users", "identity_profile", "sessions",
"session_summaries", "conversation_chunks", "facts",
"global_knowledge", "hypotheses", "upgrade_requests",
"gallery_images",
}
conn = get_connection()
rows = conn.execute(
@@ -201,12 +201,12 @@ class TestSchemaV6:
count = conn.execute("SELECT COUNT(*) FROM token_saves").fetchone()[0]
assert count == 0 # table exists, just empty
def test_schema_version_is_7(self, temp_db):
def test_schema_version_is_8(self, temp_db):
with db() as conn:
version = conn.execute(
"SELECT version FROM schema_version"
).fetchone()["version"]
assert version == 7
assert version == 8
# ── Token Efficiency Tracker (Feature 6) ──────────────────────────────────────
+62
View File
@@ -7,6 +7,7 @@ BIGMIND_DB_PATH + BIGMIND_USER to a fresh SQLite file per test.
import pytest
from datetime import datetime, timezone, timedelta
from bigmind import memory_store
from bigmind.db import db
from bigmind.profile_builder import compute_achievements, build_profile_data
@@ -44,6 +45,11 @@ class TestComputeAchievements:
"on_fire", "storyteller", "night_owl", "speed_thinker",
"first_handshake", "birthday", "shared_mind",
"frugal_mind", "quarter_million", "token_millionaire", "sniper",
"networker_bronze", "networker_silver", "networker_gold", "networker_platinum",
"tokensniper_bronze", "tokensniper_silver", "tokensniper_gold", "tokensniper_platinum",
"hypothesismaster_bronze", "hypothesismaster_silver", "hypothesismaster_gold", "hypothesismaster_platinum",
"memoryarchitect_bronze", "memoryarchitect_silver", "memoryarchitect_gold", "memoryarchitect_platinum",
"sessionveteran_bronze", "sessionveteran_silver", "sessionveteran_gold", "sessionveteran_platinum",
}
assert expected == ids
@@ -325,4 +331,60 @@ class TestComputeAchievements:
# At minimum: first_breath + first_handshake = 2
assert len(unlocked) >= 2
class TestTieredAchievements:
def test_networker_bronze(self):
uid = _uid()
with db() as conn:
conn.execute("INSERT INTO people (user_id, username) VALUES (?, ?)", (uid, "test"))
conn.commit()
achs = compute_achievements(uid)
bronze = next(a for a in achs if a['id'] == 'networker_bronze')
assert bronze['unlocked'] is True
assert bronze['image'].endswith('networker_bronze.png')
def test_tokensniper_silver(self):
uid = _uid()
sid = memory_store.create_session(uid)
memory_store.log_token_save(sid, uid, "big save", 60000, "grep")
achs = compute_achievements(uid)
silver = next(a for a in achs if a['id'] == 'tokensniper_silver')
assert silver['unlocked'] is True
def test_hypothesismaster_bronze(self):
uid = _uid()
sid = memory_store.create_session(uid)
for _ in range(3):
hid = memory_store.add_hypothesis(uid, sid, "test", 0.8)
memory_store.resolve_hypothesis(hid, uid, "confirmed", "yes")
achs = compute_achievements(uid)
bronze = next(a for a in achs if a['id'] == 'hypothesismaster_bronze')
assert bronze['unlocked'] is True
def test_memoryarchitect_silver(self):
uid = _uid()
for _ in range(100):
memory_store.store_fact(uid, "test", f"fact {_}")
achs = compute_achievements(uid)
silver = next(a for a in achs if a['id'] == 'memoryarchitect_silver')
assert silver['unlocked'] is True
def test_sessionveteran_bronze(self):
uid = _uid()
for _ in range(50):
sid = memory_store.create_session(uid)
_close_session(sid)
achs = compute_achievements(uid)
bronze = next(a for a in achs if a['id'] == 'sessionveteran_bronze')
assert bronze['unlocked'] is True
def test_tiered_achievements_have_image(self):
uid = _uid()
achs = compute_achievements(uid)
tiered_ids = [
f"{cat}_{tier}" for cat in ["networker", "tokensniper", "hypothesismaster", "memoryarchitect", "sessionveteran"]
for tier in ["bronze", "silver", "gold", "platinum"]
]
for tid in tiered_ids:
a = next(aa for aa in achs if aa['id'] == tid)
assert a['image'] is not None
assert a['image'].endswith(tid + '.png')
+199
View File
@@ -0,0 +1,199 @@
# mcp-image-gen — Architecture Assessment
**Date:** 2026-04-04
**Author:** Lumen (for Patrick / pplate)
**Status:** ✅ APPROVED — ready for implementation
**BigMind Research Session:** `39809470-6ac8-4713-adf2-79ac0eb36ba7`
---
## 1. Problem Statement
LLM agents (Claude, local models via Ollama) have no native ability to generate images. While
language models excel at text, creative and technical workflows increasingly need image output —
concept art, diagrams, product mockups, illustrations — all driven by a text prompt.
A FastMCP wrapper around a local image generation backend would give any MCP-capable IDE or
agent the ability to produce images on demand, with full control over resolution, steps, model,
and seed — without sending data to external cloud APIs.
**Gap being filled:** Local AI image generation accessible to LLM agents via MCP protocol,
running entirely on Patrick's AMD RX 7900 XTX (24GB VRAM) with ROCm.
---
## 2. Requirements
### 2.1 Functional Requirements
| ID | Requirement |
|----|-------------|
| F-1 | Generate an image from a text prompt |
| F-2 | Support configurable resolution (width × height) |
| F-3 | Support configurable inference steps and seed for reproducibility |
| F-4 | Support negative prompts to exclude unwanted content |
| F-5 | List available models from the backend |
| F-6 | Check the status of an in-progress generation job |
| F-7 | Return generated image as both a file path AND inline base64 for agent display |
| F-8 | Configure output directory for saved images |
| F-9 | Support FLUX.1-schnell as the default model |
### 2.2 Non-Functional Requirements
| ID | Requirement |
|----|-------------|
| NF-1 | Generation time < 30 seconds for FLUX.1-schnell at 1024×1024, 4 steps |
| NF-2 | VRAM footprint < 12GB (leaves headroom on 24GB for Ollama co-existence) |
| NF-3 | Must work on AMD ROCm — no CUDA-only dependencies in the MCP server layer |
| NF-4 | No cloud API calls — fully local execution |
| NF-5 | Graceful error messages when ComfyUI is not running |
| NF-6 | MCP tools must work with FastMCP and be discoverable by Claude / Roo Code |
---
## 3. Technology Decision
### 3.1 Candidate Backends
| Backend | Stars | ROCm | REST API | FLUX Support | Verdict |
|---------|-------|------|----------|--------------|---------|
| **ComfyUI** | 108k | ✅ Native | ✅ localhost:8188 | ✅ FLUX.1-schnell, FLUX.1-dev | ✅ **CHOSEN** |
| stable-diffusion.cpp | ~15k | ✅ ROCm/Vulkan | ❌ CLI only | ✅ FLUX.1-schnell | ⚠️ Viable alternative |
| PyTorch + diffusers | — | ✅ ROCm 7.2.1 | ❌ No REST | ✅ All models | ❌ Too complex to manage |
| Ollama image gen | — | ❌ Linux: N/A | ✅ /api/generate | ✅ FLUX.2, Z-Image | ❌ macOS-only as of April 2026 |
| A1111 / Forge WebUI | — | ⚠️ Limited | ✅ :7860 | ❌ SDXL primary | ❌ Not FLUX-native |
### 3.2 Why ComfyUI
1. **ROCm native** — ComfyUI's PyTorch backend runs on AMD GPUs via ROCm without forks or patches.
2. **REST API** — ComfyUI exposes a stable HTTP API at `localhost:8188` making it trivially
wrappable with `httpx`. No subprocess management or binary spawning needed.
3. **Workflow-based** — ComfyUI workflows are JSON graphs. The MCP server ships a minimal
FLUX.1-schnell workflow that can be parameterized with prompt, size, steps, seed at runtime.
4. **Model ecosystem** — ComfyUI's model manager supports FLUX.1, SDXL, SD3.5, ControlNet,
LoRA — giving a future-proof upgrade path.
5. **Community size** — 108k GitHub stars; extensive community support, model nodes, extensions.
6. **VRAM efficiency** — FLUX.1-schnell requires ~8GB VRAM. Patrick's 24GB card runs it
comfortably alongside Ollama.
### 3.3 Why NOT the Alternatives
- **Ollama:** Definitively blocked on Linux until further notice. No ETA for Linux image gen.
- **stable-diffusion.cpp:** CLI-based only — the MCP server would need to manage a subprocess,
parse stdout, handle crashes. More fragile than an HTTP API.
- **PyTorch + diffusers direct:** Requires managing Python environments, device placement, model
loading, memory management inside the MCP server process — adds significant complexity and
risk of VRAM conflicts.
---
## 4. Architecture Decision
### 4.1 System Overview
```
┌─────────────────────────────────────────────────────────┐
│ LLM Agent (Claude / Roo Code / local Ollama) │
└───────────────────────────┬─────────────────────────────┘
│ MCP Protocol (stdio)
┌───────────────────────────▼─────────────────────────────┐
│ mcp-image-gen (FastMCP Python server) │
│ │
│ Tools: │
│ • generate_image(prompt, width, height, steps, ...) │
│ • list_available_models() │
│ • get_generation_status(prompt_id) │
│ • get_output_directory() │
└───────────────────────────┬─────────────────────────────┘
│ HTTP REST (httpx)
┌───────────────────────────▼─────────────────────────────┐
│ ComfyUI (localhost:8188) │
│ AMD ROCm + PyTorch │
│ FLUX.1-schnell model │
└─────────────────────────────────────────────────────────┘
┌───────▼───────┐
│ ~/Pictures/ │
│ mcp-generated│
└───────────────┘
```
### 4.2 Key Decisions
| Decision | Choice | Rationale |
|----------|--------|-----------|
| HTTP client | `httpx` (async) | Already used in webscraper; async-friendly; clean timeout handling |
| Image return | dual: path + base64 | File path for persistence; base64 `ImageContent` for inline Claude display |
| ImageContent type | `mcp.types.ImageContent` | FastMCP 3.x: **never** use `fastmcp.utilities.types.Image` with `-> Image` annotation — it breaks serialization. Return `ImageContent` directly as a `ContentBlock`. |
| Job polling | loop with sleep | ComfyUI `/api/queue` returns pending/running/done status; poll until done or timeout |
| Workflow format | ComfyUI API JSON | Minimal FLUX.1-schnell graph parameterized at runtime |
| Config | env vars | `COMFYUI_URL`, `IMAGE_OUTPUT_DIR` — no hardcoded paths |
| Output naming | `{timestamp}_{seed}.png` | Reproducible, collision-free, sortable |
---
## 5. Risks
| Risk | Likelihood | Impact | Mitigation |
|------|------------|--------|------------|
| ComfyUI not running when tool is called | High | High | Return clear error: "ComfyUI not reachable at {url}. Start with: `python main.py --listen`" |
| Generation timeout (>60s) | Medium | Medium | Configurable timeout; return partial status message with `prompt_id` so agent can poll manually |
| VRAM contention with Ollama | Medium | Medium | FLUX.1-schnell uses ~8GB; 24GB card has 16GB headroom. Document that running both simultaneously may compete at >8GB Ollama model sizes |
| ROCm driver instability | Low | High | ComfyUI falls back to CPU if ROCm unavailable — slow but functional. Document ROCm setup. |
| ComfyUI API changes | Low | Medium | Pin ComfyUI version in setup docs; the `/api/prompt`, `/api/queue`, `/api/view` endpoints are stable |
| Large output files | Low | Low | PNG default; add optional JPEG quality param in v2 |
| Malformed workflow JSON | Low | High | Ship a tested, minimal FLUX.1-schnell workflow; validate before submit |
---
## 6. Alternatives Considered
### 6.1 Ollama (Blocked)
Ollama added image generation in January 2026 (Z-Image Turbo, FLUX.2 Klein) but the feature is
**macOS-only** as of April 2026. Linux support is listed as "coming soon" with no ETA. This was
the originally preferred path (uniform API with text generation), but it is not viable on Fedora
Linux today.
**Migration path:** When Ollama Linux image gen ships, a thin backend adapter can be added to
`mcp-image-gen` so it routes to Ollama instead of ComfyUI — same MCP tool signatures, different
HTTP target.
### 6.2 stable-diffusion.cpp
DiffuGen MCP server uses this approach. Requires:
- Building sd.cpp with ROCm/Vulkan flags
- Spawning a subprocess and parsing CLI output
- No REST API — process management in Python
Viable but more fragile than ComfyUI's HTTP API. Chosen only if ComfyUI proves unworkable.
### 6.3 diffusers (Python library, direct)
Would run diffusion pipeline inside the MCP server process. Problems:
- MCP server process cannot easily share GPU memory with Ollama
- Model loading adds 5-15s cold start to every MCP invocation
- Complex device placement / fp16 / ROCm configuration in server code
- Risk: VRAM OOM crashes the MCP server process entirely
---
## 7. Success Criteria
| Criterion | Measure |
|-----------|---------|
| `generate_image` returns a valid PNG | File exists on disk, base64 decodes to valid PNG bytes |
| Claude can display the image inline | `ImageContent` returned in tool response, visible in Roo Code chat |
| FLUX.1-schnell at 1024×1024 4-step completes in <30s | Measured on RX 7900 XTX with ROCm |
| `list_available_models` returns ComfyUI model list | At minimum includes `flux1-schnell.safetensors` |
| ComfyUI offline → clear error, not crash | Tool returns error string, no MCP server exception |
| All pytest tests pass | `uv run pytest tests/ -v` exits 0 with ≥80% coverage |
| Server wired into `.roo/mcp.json` | Tool appears in Roo Code MCP tool list |
---
## 8. Open Questions
| # | Question | Owner | Priority |
|---|----------|-------|----------|
| Q1 | Should `generate_image` be synchronous (block until done) or return a `prompt_id` immediately? | Patrick | High — MVP will be synchronous; async polling is v2 |
| Q2 | Default output directory: `~/Pictures/mcp-generated` or `~/mcp-images`? | Patrick | Low — configurable via env var |
| Q3 | Should we support SDXL as a second model in v1, or FLUX.1-schnell only? | Patrick | Low — FLUX.1-schnell only for v1 |
| Q4 | WebSocket API vs REST polling for job status? | — | ComfyUI has both; REST polling is simpler for v1 |
@@ -0,0 +1,319 @@
# Assessment: Expand `generate_image` with `name` and `count` Parameters
*Author: Lumen | Date: 2026-04-06 | Ticket: —*
*BigMind Session: `00070c37-b013-4342-a8ae-f81da0e3180d`*
*Status: 🔵 DRAFT — awaiting Patrick review*
---
## 1. Problem Statement
The current [`generate_image()`](mcp/mcp-image-gen/src/server.py:133) tool generates a single image and saves it with an auto-generated filename of `{timestamp}_{seed}.png`. Two common workflows are not yet supported:
1. **Named outputs** — When generating thematic sets (Lumen profile images, wiki banners, concept art), the caller wants a meaningful prefix in the filename (e.g., `lumen_profile_20260406_140236_2409122067.png`) rather than a bare timestamp. This also enables grouping output by purpose in the directory listing.
2. **Batch generation** — Generating multiple variations of the same prompt in one tool call is a common creative workflow. Currently, the caller must invoke `generate_image` N times with separate tool calls, which is verbose and loses the semantic grouping.
**Goal:** Add two optional parameters — `name` (filename prefix string) and `count` (integer repetitions) — to `generate_image` with minimal disruption to existing behaviour and test coverage.
---
## 2. Requirements
### 2.1 Functional Requirements
| ID | Requirement |
|----|-------------|
| F-1 | `name` parameter (default `""`) prepends a sanitized label to the output filename |
| F-2 | When `name=""` (default), filename format is unchanged: `{timestamp}_{seed}.png` |
| F-3 | When `name="lumen_profile"`, filename format is: `lumen_profile_{timestamp}_{seed}.png` |
| F-4 | `count` parameter (default `1`) generates N images sequentially |
| F-5 | When `count=1` (default), return value is identical to the current `[TextContent, ImageContent]` |
| F-6 | When `count=N > 1`, return value is a flat list: `[Text1, Image1, Text2, Image2, ..., TextN, ImageN]` |
| F-7 | When `count>1` and `seed=-1`, each image gets an independently random seed |
| F-8 | When `count>1` and a fixed `seed` is provided, images use `seed`, `seed+1`, `seed+2`, … to produce deterministic variation |
| F-9 | `count` is capped at a maximum (proposed: 10) to prevent runaway generation |
| F-10 | `name` is sanitized: non-alphanumeric characters (except `-` and `_`) are stripped/replaced; max 64 chars |
| F-11 | Partial success: if one image in a batch fails, the error is returned as a `TextContent` error item in that position rather than aborting the whole batch |
| F-12 | The TextContent for each image in a batch includes the 1-of-N index: `[1/3] Generated: ...` |
### 2.2 Non-Functional Requirements
| ID | Requirement |
|----|-------------|
| NF-1 | Sequential generation — no concurrent ComfyUI submissions (ComfyUI queues internally; parallel MCP submissions would complicate polling) |
| NF-2 | Backward compatibility — all existing callers with no `name`/`count` args produce identical output |
| NF-3 | All existing 19 tests must continue to pass without modification |
| NF-4 | New tests must cover: name prefix in filename, count=2 success, count with fixed seed increments, count with partial failure, name sanitization, count cap enforcement |
| NF-5 | MCP tool schema (visible in Claude/Roo Code) must surface clear descriptions for the new params |
---
## 3. Affected Files
| File | Change Type | Description |
|------|-------------|-------------|
| [`mcp/mcp-image-gen/src/server.py`](mcp/mcp-image-gen/src/server.py:133) | Modify | Add `name: str = ""` and `count: int = 1` params to `generate_image()`; add `_sanitize_name()` helper; extract `_generate_single()` inner logic |
| [`mcp/mcp-image-gen/tests/test_server.py`](mcp/mcp-image-gen/tests/test_server.py:1) | Modify | Add 6+ new test cases covering new parameters |
| [`mcp/mcp-image-gen/README.md`](mcp/mcp-image-gen/README.md) | Modify | Update `generate_image` tool documentation table |
| [`docs/wiki/pages/mcp-image-gen.md`](docs/wiki/pages/mcp-image-gen.md) | Modify | Update tool reference table with new parameters |
No schema changes, no new dependencies, no workflow JSON changes.
---
## 4. Design Decisions
### 4.1 Filename Convention with `name`
**Current:** `{timestamp}_{seed}.png`
**Proposed:** `{sanitized_name}_{timestamp}_{seed}.png` (when `name` is provided)
The `name` is placed as a **prefix** rather than suffix so directory `ls` output groups named sets together alphabetically:
```
lumen_profile_20260406_140236_2409122067.png
lumen_profile_20260406_140258_764633840.png
wiki_banner_20260406_141000_1234567.png
```
**Sanitization rule:** `re.sub(r'[^a-zA-Z0-9_-]', '_', name)[:64]` — replaces any character that is not alphanumeric, dash, or underscore with `_`, then truncates to 64 chars.
### 4.2 Seed Behaviour for Batch Generation
| Scenario | Behaviour |
|----------|-----------|
| `count=3, seed=-1` | Each call to `build_flux_workflow` gets `seed=-1` → 3 independent random seeds |
| `count=3, seed=42` | Seeds are 42, 43, 44 — deterministic, reproducible variation |
This follows the convention of most image generation tools (e.g., ComfyUI's own batch seed increment).
### 4.3 Return Structure for `count > 1`
Return a **flat interleaved list**: `[Text1, Image1, Text2, Image2]`
**Rationale:** MCP content lists are flat arrays. Claude/Roo Code renders them sequentially — a flat list means each image appears immediately below its metadata line. A nested structure would require the caller to unwrap it.
**For `count=1` (default):** Behaviour is identical to today — `[TextContent, ImageContent]`. No caller breakage.
### 4.4 Refactoring: Extract `_generate_single()`
The current `generate_image` function is 180+ lines of inline logic. To support `count`, the inner pipeline (queue → poll → history → download → save → encode) will be extracted to a private `async def _generate_single(prompt, ..., index, total)` coroutine. `generate_image` then loops `count` times calling `_generate_single` and accumulates results.
This refactoring:
- Makes the count loop clean (`results.extend(await _generate_single(...))`)
- Makes partial failure handling straightforward (catch per iteration)
- Improves testability of the single-image path
### 4.5 Maximum Count Cap
Cap `count` at **10**. Rationale:
- FLUX.1-schnell takes ~1035s per image on RX 7900 XTX → 10 images ≈ 100350s maximum
- MCP tool call timeout in Roo Code defaults to 5 minutes — 10 images is safe margin
- ComfyUI queues them internally; the MCP server polls sequentially, not in parallel
When `count > 10`, the tool returns a single `TextContent` error immediately (no images generated) with message: `"count={N} exceeds maximum of 10. Reduce count and retry."`
---
## 5. Implementation Plan
### Step 1 — Add `_sanitize_name()` helper
```python
import re
def _sanitize_name(name: str) -> str:
"""Sanitize a name for use as a filename prefix."""
sanitized = re.sub(r'[^a-zA-Z0-9_-]', '_', name)
return sanitized[:64]
```
Location: [`server.py`](mcp/mcp-image-gen/src/server.py:95), after `build_flux_workflow()` (pure function section).
### Step 2 — Extract `_generate_single()` coroutine
Extract the body of the current `generate_image` (lines 162310) into:
```python
async def _generate_single(
prompt: str,
width: int,
height: int,
steps: int,
model: str,
seed: int,
negative_prompt: str,
resolved_output_dir: Path,
filename_prefix: str,
index: int,
total: int,
) -> list:
```
The `filename` construction changes to:
```python
filename = f"{filename_prefix}{timestamp}_{actual_seed}.png"
# where filename_prefix = f"{sanitized_name}_" if sanitized_name else ""
```
The `TextContent` text changes when `total > 1`:
```python
prefix_label = f"[{index}/{total}] " if total > 1 else ""
text = f"{prefix_label}Generated: {out_path}\nSeed: ..."
```
### Step 3 — Update `generate_image()` signature
```python
@mcp.tool()
async def generate_image(
prompt: str,
width: int = 1024,
height: int = 1024,
steps: int = 4,
model: str = "flux1-schnell.safetensors",
seed: int = -1,
negative_prompt: str = "",
output_dir: str = "",
name: str = "",
count: int = 1,
) -> list:
```
Body of `generate_image` becomes:
```python
# Validate count
MAX_COUNT = 10
if count < 1 or count > MAX_COUNT:
return [TextContent(type="text", text=f"count={count} is invalid. Must be 1{MAX_COUNT}.")]
sanitized_name = _sanitize_name(name) if name else ""
filename_prefix = f"{sanitized_name}_" if sanitized_name else ""
resolved_output_dir = Path(output_dir or IMAGE_OUTPUT_DIR).expanduser().resolve()
results = []
for i in range(1, count + 1):
actual_seed = seed if seed == -1 else seed + (i - 1)
items = await _generate_single(
prompt=prompt, width=width, height=height, steps=steps,
model=model, seed=actual_seed, negative_prompt=negative_prompt,
resolved_output_dir=resolved_output_dir,
filename_prefix=filename_prefix, index=i, total=count,
)
results.extend(items)
return results
```
### Step 4 — Write new tests
Add to [`test_server.py`](mcp/mcp-image-gen/tests/test_server.py:550):
| Test | Description |
|------|-------------|
| `test_generate_image_with_name` | `name="lumen"` → filename starts with `lumen_` |
| `test_generate_image_name_sanitization` | `name="my image! v2"``my_image__v2_` prefix |
| `test_generate_image_count_2_success` | `count=2` → 4 items in result, 2 files saved |
| `test_generate_image_count_fixed_seed` | `count=2, seed=42` → seeds 42 and 43 in filenames |
| `test_generate_image_count_partial_failure` | `count=2`, second POST fails → 2 items (success) + 1 item (error) |
| `test_generate_image_count_cap_exceeded` | `count=11` → single TextContent error, no generation |
| `test_generate_image_count_0_invalid` | `count=0` → single TextContent error |
| `test_generate_image_name_and_count_combined` | `name="banner", count=2` → both files prefixed `banner_` |
### Step 5 — Update documentation
- Update `generate_image` docstring in [`server.py`](mcp/mcp-image-gen/src/server.py:144) to document `name` and `count`
- Update parameter table in [`README.md`](mcp/mcp-image-gen/README.md)
- Update tool reference in [`docs/wiki/pages/mcp-image-gen.md`](docs/wiki/pages/mcp-image-gen.md)
### Step 6 — Run full test suite
```bash
cd mcp/mcp-image-gen && uv run pytest tests/ -v --tb=short
```
All 19 existing + 8 new = **27 tests** must pass.
### Step 7 — Commit and push
Branch: `feat/mcp-image-gen/generate-image-name-count`
Commit: `feat(mcp-image-gen): add name and count params to generate_image`
---
## 6. Risks
| Risk | Likelihood | Impact | Mitigation |
|------|------------|--------|------------|
| Partial batch failure leaves orphaned files on disk | Medium | Low | Files for successful images are kept; error TextContent clearly identifies which index failed. No cleanup needed — partial results are useful. |
| `count` loop adds significant latency visible in Roo Code | Medium | Medium | Document expected time: `count × ~15s`. MCP timeout is 5 min; max 10 images ≈ 150s. Still within limit. |
| Seed increment wraps around at `2^32` | Very Low | Low | `(seed + i - 1) % 2**32` — add modulo guard in `_generate_single` |
| `_generate_single` refactor introduces regression in existing tests | Low | High | Existing test fixtures mock ComfyUI endpoints — as long as the HTTP call sequence is unchanged, respx mocks will match. Verify each existing test still passes before adding new ones. |
| `name` with only special chars becomes empty after sanitization | Low | Medium | After sanitization, if result is empty string, treat as unnamed (no prefix). Add assertion in `_sanitize_name` to return `""` for all-whitespace/special inputs. |
| MCP tool schema change breaks existing callers | Very Low | Low | New params are optional with defaults — backward compatible. Roo Code re-reads schema on server restart. |
---
## 7. Alternatives Considered
### 7.1 Separate `generate_images_batch()` Tool (Rejected)
Add a new tool instead of expanding `generate_image`.
**Pros:** Clean separation, no refactoring of existing tool.
**Cons:** Two tools for the same backend; callers must learn two tool names; MCP tool list grows. The MCP convention favours extending existing tools with optional parameters rather than proliferating tools.
**Verdict:** Rejected. Optional parameters with backward-compatible defaults is the right pattern here.
### 7.2 Return Grouped List of Lists for `count > 1` (Rejected)
Return `[[Text1, Image1], [Text2, Image2]]` for batch results.
**Pros:** Caller can index by image number cleanly.
**Cons:** MCP content type is a flat `list[ContentBlock]`. FastMCP does not support nested lists in tool returns — they would be serialized as strings, not rendered. Roo Code renders content sequentially; flat interleaved is the idiomatic structure.
**Verdict:** Rejected. Flat interleaved list `[Text1, Image1, Text2, Image2]` is MCP-idiomatic.
### 7.3 Parallel ComfyUI Submission for Batch (Rejected)
Submit all `count` prompts to ComfyUI simultaneously (async tasks), then collect results in order.
**Pros:** Faster if ComfyUI supports parallel queue processing (it does).
**Cons:** ComfyUI processes one job at a time on a single GPU regardless — parallel submission just fills the queue. Polling becomes complex (N polling loops). Error handling harder. Out-of-order completions break index alignment.
**Verdict:** Rejected for v1. Sequential submission is simpler, correct, and produces no worse throughput. Can revisit if ComfyUI gains true parallel processing support.
### 7.4 Name as Subdirectory Instead of Filename Prefix (Rejected)
When `name="lumen"`, save to `output_dir/lumen/` instead of `output_dir/lumen_*.png`.
**Pros:** Better directory organisation for large sets.
**Cons:** Complicates the implementation (directory creation per name), changes the return path format, breaks callers who assume a flat output directory. Adds complexity for minimal gain at `count ≤ 10`.
**Verdict:** Rejected for v1. Prefix approach is simpler and equally readable.
---
## 8. Success Criteria
| Criterion | Measure |
|-----------|---------|
| All 27 tests pass | `uv run pytest tests/ -v` exits 0 |
| `name="lumen"` → file starts with `lumen_` | Assert in `test_generate_image_with_name` |
| `count=2` → 4 content items, 2 files | Assert `len(result) == 4`, `len(glob("*.png")) == 2` |
| `count=2, seed=42` → seeds 42 and 43 | Assert seed values in TextContent |
| `count=11` → error TextContent, no ComfyUI call | Assert `len(result) == 1`, no `/api/prompt` mock hit |
| Backward compat: existing callers unaffected | All 19 existing tests pass without modification |
| MCP tool schema shows `name` and `count` params | Visible in Roo Code tool list after server restart |
---
## 9. Open Questions
| # | Question | Owner | Priority |
|---|----------|-------|----------|
| Q1 | Should `count=0` be an error, or silently return `[]` (empty list)? | Patrick | Low — assessment recommends error for clarity |
| Q2 | Max count cap: 10 or higher? 10 ≈ 150s max at 15s/image — feels right, but could be raised to 20 for batch profile image sets. | Patrick | Medium |
| Q3 | Should partial batch failure stop remaining iterations, or always complete all N? | Patrick | Medium — assessment recommends continue (partial success) |
| Q4 | Should `name` parameter also tag the TextContent output text, e.g. `[lumen_profile 1/3] Generated: ...`? | Patrick | Low |
+496
View File
@@ -0,0 +1,496 @@
# mcp-image-gen — Implementation Plan
**Date:** 2026-04-04
**Author:** Lumen (for Patrick / pplate)
**Status:** Ready for implementation
**Assessment:** [ASSESSMENT.md](./ASSESSMENT.md)
**Research Session:** `39809470-6ac8-4713-adf2-79ac0eb36ba7`
---
## 1. Directory Structure
```
mcp/mcp-image-gen/
├── ASSESSMENT.md ← Architecture assessment (this session)
├── PLAN.md ← This file
├── README.md ← Usage docs, tool table, env vars
├── pyproject.toml ← uv project + deps
├── run.sh ← Launch script (used by .roo/mcp.json)
├── src/
│ ├── __init__.py
│ ├── server.py ← FastMCP server + all tools
│ └── workflows/
│ └── flux_schnell.json ← Minimal ComfyUI API-format workflow
└── tests/
├── __init__.py
├── conftest.py ← sys.path + shared fixtures
└── test_server.py ← All tool tests (mocked ComfyUI)
```
---
## 2. Tool Definitions
### 2.1 `generate_image`
```python
@mcp.tool()
async def generate_image(
prompt: str,
width: int = 1024,
height: int = 1024,
steps: int = 4,
model: str = "flux1-schnell.safetensors",
seed: int = -1,
negative_prompt: str = "",
output_dir: str = "",
) -> list:
"""
Generate an image from a text prompt using ComfyUI.
Returns both a file path (for persistence) and an inline base64 image
(for display in Claude / Roo Code chat).
Args:
prompt: Text description of the image to generate.
width: Image width in pixels (default: 1024).
height: Image height in pixels (default: 1024).
steps: Number of inference steps. FLUX.1-schnell works well at 4.
model: ComfyUI model filename (default: flux1-schnell.safetensors).
seed: Random seed for reproducibility. -1 = random.
negative_prompt: Things to exclude from the image (optional).
output_dir: Override output directory. Defaults to IMAGE_OUTPUT_DIR env var
or ~/Pictures/mcp-generated.
Returns:
[TextContent(path + metadata), ImageContent(base64 PNG)]
"""
```
**Return type:** `list` containing:
1. `mcp.types.TextContent` — human-readable summary with file path, seed, elapsed time
2. `mcp.types.ImageContent``type="image"`, `data=base64_encoded_png`, `mimeType="image/png"`
> ⚠️ **FastMCP 3.x rule:** NEVER annotate return as `-> Image` (fastmcp utility type). It triggers
> `output_schema` generation which breaks the early-return path. Return `mcp.types.ImageContent`
> directly as part of a `list` — it is a `ContentBlock` and passes through cleanly.
---
### 2.2 `list_available_models`
```python
@mcp.tool()
async def list_available_models() -> str:
"""
List all checkpoint models available in ComfyUI.
Returns a newline-separated list of model filenames.
Requires ComfyUI to be running at COMFYUI_URL.
"""
```
**Implementation:** `GET {COMFYUI_URL}/object_info/CheckpointLoaderSimple` → parse
`input.required.ckpt_name[0]` list → join with newlines.
---
### 2.3 `get_generation_status`
```python
@mcp.tool()
async def get_generation_status(prompt_id: str) -> str:
"""
Check the status of a queued or running generation job.
Args:
prompt_id: The prompt ID returned by a previous generate_image call.
Returns:
Status string: "pending", "running", "completed", or "not_found".
"""
```
**Implementation:** `GET {COMFYUI_URL}/api/queue` → check `queue_running` and `queue_pending`
lists for matching `prompt_id`. If not found in either, check history endpoint.
---
### 2.4 `get_output_directory`
```python
@mcp.tool()
def get_output_directory() -> str:
"""
Return the directory where generated images are saved.
Returns:
Absolute path to the output directory.
"""
```
**Implementation:** Resolve `IMAGE_OUTPUT_DIR` env var or default `~/Pictures/mcp-generated`,
expand `~`, return as string.
---
## 3. ComfyUI Integration
### 3.1 Workflow: Submit → Poll → Retrieve
```
generate_image()
├── 1. Load flux_schnell.json workflow template
├── 2. Parameterize: inject prompt, width, height, steps, seed, model
├── 3. POST {COMFYUI_URL}/api/prompt → {"prompt_id": "uuid"}
├── 4. POLL loop (max 120s, sleep 2s between)
│ GET {COMFYUI_URL}/api/queue
│ → check queue_running[].prompt_id == our id
│ → check queue_pending[].prompt_id == our id
│ → if neither: job is done
├── 5. GET {COMFYUI_URL}/api/history/{prompt_id}
│ → find output image filename + subfolder
├── 6. GET {COMFYUI_URL}/api/view?filename={name}&subfolder={subfolder}&type=output
│ → raw PNG bytes
├── 7. Save PNG to output_dir/{timestamp}_{seed}.png
└── 8. Return [TextContent(path + meta), ImageContent(base64)]
```
### 3.2 API Endpoints Used
| Endpoint | Method | Purpose |
|----------|--------|---------|
| `/api/prompt` | POST | Submit workflow for generation |
| `/api/queue` | GET | Poll queue status (pending + running) |
| `/api/history/{prompt_id}` | GET | Get completed job output filenames |
| `/api/view` | GET | Download image bytes by filename |
| `/object_info/CheckpointLoaderSimple` | GET | List available checkpoint models |
### 3.3 Error Handling
| Condition | Response |
|-----------|----------|
| ComfyUI unreachable | `"ComfyUI not reachable at {url}. Start it with: python main.py --listen"` |
| Timeout (>120s) | `"Generation timed out after 120s. prompt_id={id} — use get_generation_status to check"` |
| ComfyUI returns error in history | Extract and return the error message from history response |
| Invalid model name | ComfyUI returns error in history; surface it clearly |
| Output dir not writable | `"Cannot write to output directory: {path}"` |
---
## 4. Configuration
All configuration via environment variables. No hardcoded paths.
| Variable | Default | Description |
|----------|---------|-------------|
| `COMFYUI_URL` | `http://localhost:8188` | Base URL of running ComfyUI instance |
| `IMAGE_OUTPUT_DIR` | `~/Pictures/mcp-generated` | Where to save generated PNG files |
| `COMFYUI_TIMEOUT` | `120` | Max seconds to wait for generation (int) |
### `.roo/mcp.json` entry (to be added during implementation):
```json
"mcp-image-gen": {
"command": "uv",
"args": [
"--directory", "/home/pplate/pi_mcps/mcp/mcp-image-gen",
"run", "src/server.py"
],
"env": {
"COMFYUI_URL": "http://localhost:8188",
"IMAGE_OUTPUT_DIR": "/home/pplate/Pictures/mcp-generated"
}
}
```
---
## 5. `pyproject.toml`
```toml
[project]
name = "mcp-image-gen"
version = "0.1.0"
requires-python = ">=3.11"
description = "MCP server for local AI image generation via ComfyUI"
dependencies = [
"fastmcp>=0.1.0",
"httpx>=0.27.0",
"pillow>=10.0.0",
]
[project.optional-dependencies]
test = [
"pytest>=7.0",
"pytest-mock>=3.0",
"pytest-cov>=4.0",
"pytest-asyncio>=0.23",
]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.pytest.ini_options]
asyncio_mode = "auto"
```
**Dependency rationale:**
- `fastmcp` — MCP framework
- `httpx` — async HTTP client for ComfyUI REST API
- `pillow` — validate PNG output, potential future thumbnail generation
- `pytest-asyncio` — needed for async tool tests
---
## 6. FLUX.1-schnell Workflow JSON
The minimal ComfyUI API-format workflow for FLUX.1-schnell text-to-image.
This is the "API format" (node-graph JSON), not the UI export format.
File: `src/workflows/flux_schnell.json`
```json
{
"6": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": ["30", 1],
"text": "PROMPT_PLACEHOLDER"
}
},
"8": {
"class_type": "VAEDecode",
"inputs": {
"samples": ["13", 0],
"vae": ["30", 2]
}
},
"9": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "mcp-image-gen",
"images": ["8", 0]
}
},
"13": {
"class_type": "KSampler",
"inputs": {
"cfg": 1.0,
"denoise": 1.0,
"latent_image": ["27", 0],
"model": ["30", 0],
"negative": ["33", 0],
"positive": ["6", 0],
"sampler_name": "euler",
"scheduler": "simple",
"seed": 42,
"steps": 4
}
},
"27": {
"class_type": "EmptySD3LatentImage",
"inputs": {
"batch_size": 1,
"height": 1024,
"width": 1024
}
},
"30": {
"class_type": "CheckpointLoaderSimple",
"inputs": {
"ckpt_name": "flux1-schnell.safetensors"
}
},
"33": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": ["30", 1],
"text": "NEGATIVE_PLACEHOLDER"
}
}
}
```
**Parameterization at runtime** (in `server.py`):
```python
import json, copy
def _build_workflow(prompt, negative_prompt, width, height, steps, seed, model):
with open(Path(__file__).parent / "workflows/flux_schnell.json") as f:
wf = json.load(f)
wf = copy.deepcopy(wf)
wf["6"]["inputs"]["text"] = prompt
wf["33"]["inputs"]["text"] = negative_prompt
wf["27"]["inputs"]["width"] = width
wf["27"]["inputs"]["height"] = height
wf["13"]["inputs"]["steps"] = steps
wf["13"]["inputs"]["seed"] = seed if seed != -1 else random.randint(0, 2**32 - 1)
wf["30"]["inputs"]["ckpt_name"] = model
return wf
```
---
## 7. Testing Strategy
### 7.1 Test Structure (`tests/test_server.py`)
All tests mock `httpx.AsyncClient` — no real ComfyUI needed.
| Test | Description |
|------|-------------|
| `test_generate_image_happy_path` | Mock submit → poll done → history → view → returns TextContent + ImageContent |
| `test_generate_image_comfyui_offline` | httpx.ConnectError → returns clear error string |
| `test_generate_image_timeout` | Poll loop exceeds COMFYUI_TIMEOUT → returns timeout message with prompt_id |
| `test_generate_image_saves_file` | Verify PNG written to output_dir with correct filename pattern |
| `test_generate_image_random_seed` | seed=-1 → seed in output filename is a valid integer |
| `test_generate_image_custom_params` | Non-default width/height/steps/model passed through to workflow |
| `test_generate_image_returns_image_content` | Second item in result list is `mcp.types.ImageContent` with valid base64 |
| `test_list_available_models_happy_path` | Mock object_info response → returns model name list |
| `test_list_available_models_offline` | ConnectError → returns error string |
| `test_get_generation_status_pending` | prompt_id found in queue_pending → "pending" |
| `test_get_generation_status_running` | prompt_id found in queue_running → "running" |
| `test_get_generation_status_not_found` | prompt_id not in queue, not in history → "not_found" |
| `test_get_output_directory_default` | No env var → returns expanded ~/Pictures/mcp-generated |
| `test_get_output_directory_custom` | IMAGE_OUTPUT_DIR set → returns that path |
| `test_build_workflow_parameterization` | _build_workflow() injects all params correctly into JSON |
### 7.2 conftest.py fixtures
```python
import sys
from pathlib import Path
import pytest
sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
@pytest.fixture
def mock_comfyui_submit_response():
return {"prompt_id": "test-uuid-1234"}
@pytest.fixture
def mock_comfyui_queue_empty():
return {"queue_running": [], "queue_pending": []}
@pytest.fixture
def mock_comfyui_history():
return {
"test-uuid-1234": {
"outputs": {
"9": {
"images": [{"filename": "mcp-image-gen_00001_.png", "subfolder": "", "type": "output"}]
}
}
}
}
@pytest.fixture
def sample_png_bytes():
"""Minimal valid 1x1 PNG in bytes."""
import base64
# 1x1 red pixel PNG
data = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwADhQGAWjR9awAAAABJRU5ErkJggg=="
return base64.b64decode(data)
```
### 7.3 Run command
```bash
cd mcp/mcp-image-gen && uv run pytest tests/ -v --cov=src --cov-report=term-missing
```
---
## 8. `run.sh`
```bash
#!/usr/bin/env bash
BASEDIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
export PATH="$HOME/.local/bin:$PATH"
# Create output dir if it doesn't exist
OUTPUT_DIR="${IMAGE_OUTPUT_DIR:-$HOME/Pictures/mcp-generated}"
mkdir -p "$OUTPUT_DIR"
cd "$BASEDIR"
exec uv run src/server.py
```
---
## 9. Future: Ollama Migration Path
When Ollama adds Linux image generation support (ETA unknown, announced "coming soon" April 2026):
### Adapter pattern (no breaking changes to MCP tool signatures)
```python
BACKEND = os.getenv("IMAGE_BACKEND", "comfyui") # or "ollama"
async def _generate_comfyui(prompt, width, height, steps, model, seed, negative_prompt, output_dir):
# current ComfyUI implementation
...
async def _generate_ollama(prompt, width, height, steps, model, seed, negative_prompt, output_dir):
# POST http://localhost:11434/api/generate
# with model=Z-Image-Turbo or FLUX.2-Klein
# width, height, steps in request body
# save returned image path
...
@mcp.tool()
async def generate_image(prompt, width=1024, height=1024, steps=4, ...):
if BACKEND == "ollama":
return await _generate_ollama(...)
return await _generate_comfyui(...)
```
**No changes to:** tool signatures, return types, env vars (add `IMAGE_BACKEND`), tests structure.
---
## 10. Implementation Order (for Code mode)
1. `src/workflows/flux_schnell.json` — write and validate JSON structure
2. `pyproject.toml` — set up project + deps
3. `src/__init__.py` — empty
4. `src/server.py` — implement all 4 tools + `_build_workflow` + polling helpers
5. `tests/conftest.py` — fixtures + sys.path
6. `tests/test_server.py` — all 15 tests
7. `run.sh` — launch script
8. `README.md` — usage docs
9. `.roo/mcp.json` — wire server in (requires switching to Code or Homelab mode for that file)
10. `uv sync && uv run pytest tests/ -v` — confirm all tests pass
---
## 11. ComfyUI Setup Notes (for README)
These are prerequisites for the MCP server to work. Patrick must have ComfyUI installed:
```bash
# Install ComfyUI (ROCm/AMD)
pip install comfyui
# Download FLUX.1-schnell model (~8GB)
# Place in ComfyUI/models/checkpoints/flux1-schnell.safetensors
# Source: https://huggingface.co/black-forest-labs/FLUX.1-schnell
# Start ComfyUI with AMD ROCm
HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py --listen
# Verify API is running
curl http://localhost:8188/system_stats
```
> The `HSA_OVERRIDE_GFX_VERSION=11.0.0` env var may be needed for RX 7900 XTX (gfx1100)
> to identify correctly to ROCm libraries.
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# mcp-image-gen
**FastMCP server for AI image generation via ComfyUI.**
This MCP server wraps a locally running [ComfyUI](https://github.com/comfyanonymous/ComfyUI) instance, exposing image generation as MCP tools callable from Roo Code, Claude Desktop, or any MCP-compatible client. It supports FLUX.1-schnell, FLUX.1-dev, SDXL, and any other ComfyUI-compatible checkpoint model. Generated images are saved to disk **and** returned as inline base64 so Claude can display them directly in chat.
---
## Prerequisites
1. **ComfyUI** installed and running at `http://localhost:8188`
2. At least one checkpoint model downloaded (see ComfyUI Setup below)
3. **Python 3.11+** and **uv** installed on the system
---
## Installation
```bash
cd mcp/mcp-image-gen
uv sync
```
---
## Configuration
All configuration is via environment variables:
| Variable | Default | Description |
|---|---|---|
| `COMFYUI_URL` | `http://localhost:8188` | Base URL of the running ComfyUI instance |
| `IMAGE_OUTPUT_DIR` | `~/Pictures/mcp-generated` | Directory where generated PNG files are saved |
| `COMFYUI_TIMEOUT` | `120` | Max seconds to wait for generation before timeout |
---
## Usage
### Add to `.roo/mcp.json` (Roo Code)
```json
"mcp-image-gen": {
"command": "uv",
"args": [
"--directory", "/home/pplate/pi_mcps/mcp/mcp-image-gen",
"run", "src/server.py"
],
"env": {
"COMFYUI_URL": "http://localhost:8188",
"IMAGE_OUTPUT_DIR": "/home/pplate/Pictures/mcp-generated"
}
}
```
### Add to Claude Desktop (`claude_desktop_config.json`)
```json
{
"mcpServers": {
"mcp-image-gen": {
"command": "uv",
"args": [
"--directory", "/home/pplate/pi_mcps/mcp/mcp-image-gen",
"run", "src/server.py"
],
"env": {
"COMFYUI_URL": "http://localhost:8188",
"IMAGE_OUTPUT_DIR": "/home/pplate/Pictures/mcp-generated"
}
}
}
}
```
### Run directly
```bash
cd mcp/mcp-image-gen
./run.sh
```
---
## Available Tools
| Tool | Description |
|---|---|
| `generate_image` | Generate an image from a text prompt. Returns file path + inline base64 PNG. |
| `list_available_models` | List all checkpoint models loaded in ComfyUI. |
| `get_generation_status` | Check status of a running/queued generation by `prompt_id`. |
| `get_output_directory` | Return the current output directory path. |
### `generate_image` parameters
| Parameter | Default | Description |
|---|---|---|
| `prompt` | *(required)* | Text description of the image |
| `width` | `1024` | Image width in pixels |
| `height` | `1024` | Image height in pixels |
| `steps` | `4` | Inference steps (FLUX.1-schnell: 4 is optimal) |
| `model` | `flux1-schnell.safetensors` | Checkpoint model filename |
| `seed` | `-1` | Seed for reproducibility (`-1` = random) |
| `negative_prompt` | `""` | Things to exclude from the image |
| `output_dir` | *(IMAGE_OUTPUT_DIR)* | Override output directory |
---
## ComfyUI Setup (Fedora + AMD ROCm)
```bash
# Install ComfyUI
pip install comfyui
# Download FLUX.1-schnell model (~8GB, Apache 2.0)
# Place in: ComfyUI/models/checkpoints/flux1-schnell.safetensors
# Source: https://huggingface.co/black-forest-labs/FLUX.1-schnell
# Start ComfyUI with ROCm support for AMD RX 7900 XTX
HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py --listen
# Verify the API is reachable
curl http://localhost:8188/system_stats
```
> **Note:** `HSA_OVERRIDE_GFX_VERSION=11.0.0` may be needed for the RX 7900 XTX (gfx1100)
> to be recognized correctly by ROCm libraries.
### PyTorch with ROCm (if needed separately)
```bash
pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6.1
```
---
## Testing
```bash
cd mcp/mcp-image-gen
uv run pytest tests/ -v
```
All tests mock the ComfyUI HTTP API — no running ComfyUI instance needed.
---
## Ollama Migration Path
When Ollama adds Linux image generation support (announced "coming soon" as of April 2026, currently macOS-only), this server can switch backends via a single env var:
```bash
IMAGE_BACKEND=ollama # currently only "comfyui" is implemented
```
The tool signatures, return types, and MCP interface will remain unchanged — only the underlying HTTP calls switch from ComfyUI to Ollama's `/api/generate` endpoint.
---
## Architecture
```
Roo Code / Claude Desktop
│ MCP (stdio)
mcp-image-gen (FastMCP)
│ HTTP REST
ComfyUI @ localhost:8188
│ ROCm / AMD GPU
FLUX.1-schnell / SDXL / SD3.5
```
The server submits a FLUX.1-schnell ComfyUI API-format workflow, polls until complete, downloads the PNG, saves it to disk, and returns both a text summary and a base64-encoded inline image.
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# mcp-image-gen — Usage Guide
> **Comprehensive reference for using the ComfyUI-backed image generation MCP server**
---
## Table of Contents
1. [Prerequisites — ComfyUI Setup](#1-prerequisites--comfyui-setup)
2. [Quick Start — Running the MCP Server](#2-quick-start--running-the-mcp-server)
3. [How to Ask Lumen to Generate Images](#3-how-to-ask-lumen-to-generate-images)
4. [Available Tools](#4-available-tools)
5. [Parameters Reference](#5-parameters-reference)
6. [Output Format](#6-output-format)
7. [Environment Variables](#7-environment-variables)
8. [Test Status](#8-test-status)
9. [Prompt Tips for FLUX.1-schnell](#9-prompt-tips-for-flux1-schnell)
10. [Known Limitations](#10-known-limitations)
---
## 1. Prerequisites — ComfyUI Setup
### ComfyUI must be running before any image generation tool call succeeds.
The MCP server connects to ComfyUI's REST API at `http://localhost:8188`. If ComfyUI is not running, `generate_image` and `list_available_models` will return a graceful error message — no crash.
### Install ComfyUI
> ⚠️ **ComfyUI is NOT on PyPI** — `pip install comfyui` will fail with "No matching distribution found".
> It must be installed from source via `git clone`.
```bash
# Clone from source (the only correct installation method)
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
pip install -r requirements.txt
```
### Install PyTorch with ROCm (AMD RX 7900 XTX)
Patrick's RX 7900 XTX (gfx1100, 24GB VRAM) uses the ROCm backend. Standard CUDA builds **will not work** on AMD hardware.
```bash
# PyTorch with ROCm 6.1 support
pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6.1
```
> **ROCm version note:** ROCm 7.2.1 is the current production release as of April 2026.
> Check `rocm-smi` to confirm your ROCm version before installing torch.
### Download FLUX.1-schnell (Primary Model)
FLUX.1-schnell is the recommended model — fast (4 steps), Apache 2.0 licensed, excellent quality.
> ⚠️ **FLUX.1-schnell is a gated model on HuggingFace.**
> A bare `wget` on the URL returns HTTP 401. You must:
> 1. Accept the license at https://huggingface.co/black-forest-labs/FLUX.1-schnell (click **"Agree and access repository"** — one-time)
> 2. Create a HuggingFace access token with **Read** permissions at https://huggingface.co/settings/tokens
#### Option A — `huggingface-cli` (recommended)
```bash
# Install the HuggingFace Hub CLI
pip install huggingface_hub
# Log in — paste your Read token when prompted
huggingface-cli login
# Download (~8GB) directly into ComfyUI checkpoints
huggingface-cli download black-forest-labs/FLUX.1-schnell \
flux1-schnell.safetensors \
--local-dir ~/ComfyUI/models/checkpoints/
```
#### Option B — `wget` with Authorization header
```bash
wget --header="Authorization: Bearer hf_YOUR_TOKEN_HERE" \
https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/flux1-schnell.safetensors \
-O ~/ComfyUI/models/checkpoints/flux1-schnell.safetensors
```
> Replace `hf_YOUR_TOKEN_HERE` with your actual HuggingFace token from https://huggingface.co/settings/tokens
#### Alternative: fp8 quantized variant (~8.1GB, faster inference)
If you want slightly faster inference with near-identical quality, the fp8 quantized version is also available:
```bash
huggingface-cli download black-forest-labs/FLUX.1-schnell-fp8 \
flux1-schnell-fp8.safetensors \
--local-dir ~/ComfyUI/models/checkpoints/
```
> **Download note:** Both variants are ~8GB — expect 1030 minutes depending on connection speed.
You'll also need the CLIP and VAE models — see the [ComfyUI FLUX guide](https://github.com/comfyanonymous/ComfyUI/blob/master/README.md) for full model list.
### Start ComfyUI (AMD ROCm)
```bash
# Standard start — listens on all interfaces at port 8188
HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py --listen
# Or with explicit port
HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py --listen --port 8188
```
> **`HSA_OVERRIDE_GFX_VERSION=11.0.0`** — Required for RX 7900 XTX (gfx1100).
> Without this, ROCm may fail to detect the GPU correctly. This tells the HIP runtime
> to treat the GPU as gfx1100 architecture.
### Verify ComfyUI is Running
```bash
curl -s http://localhost:8188/system_stats | python3 -m json.tool | head -20
```
Expected response includes `system` object with `python_version`, `pytorch_version`, `embedded_python`, and `comfyui_version`.
---
## 2. Quick Start — Running the MCP Server
### Via `run.sh` (recommended)
```bash
cd /home/pplate/pi_mcps/mcp/mcp-image-gen
./run.sh
```
[`run.sh`](run.sh) automatically:
- Sets `PATH` to include `~/.local/bin` for `uv`
- Creates `IMAGE_OUTPUT_DIR` (`~/Pictures/mcp-generated`) if it doesn't exist
- Launches the FastMCP server via `uv run src/server.py` (stdio transport)
### Via uv directly
```bash
cd /home/pplate/pi_mcps/mcp/mcp-image-gen
uv run src/server.py
```
### Wired into `.roo/mcp.json`
The server is already configured in [`.roo/mcp.json`](../../.roo/mcp.json):
```json
"mcp-image-gen": {
"command": "uv",
"args": [
"--directory", "/home/pplate/pi_mcps/mcp/mcp-image-gen",
"run", "src/server.py"
],
"env": {
"COMFYUI_URL": "http://localhost:8188",
"IMAGE_OUTPUT_DIR": "/home/pplate/Pictures/mcp-generated"
}
}
```
Roo Code / Claude Desktop will auto-start the server when any image generation tool is invoked. The MCP server itself starts in ~1 second — ComfyUI must already be running separately.
### Install dependencies (first time)
```bash
cd /home/pplate/pi_mcps/mcp/mcp-image-gen
uv sync
```
---
## 3. How to Ask Lumen to Generate Images
Just speak naturally. Lumen will call the appropriate MCP tool automatically.
### Basic generation
> *"Generate an image of a futuristic city at sunset"*
```
→ generate_image(prompt="futuristic city at sunset", width=1024, height=1024, steps=4)
```
### Specific style and size
> *"Create a portrait of a red fox in watercolor style, 1024x1024"*
```
→ generate_image(
prompt="portrait of a red fox, watercolor style, detailed fur, soft brushstrokes",
width=1024, height=1024
)
```
### Reproducible with a fixed seed
> *"Make an image with seed 42 so I can reproduce it"*
```
→ generate_image(prompt="...", seed=42)
```
The seed is reported in the text output so you can use the same seed again.
### Landscape format
> *"Generate a wide cinematic landscape of a Norwegian fjord, 1920x1080"*
```
→ generate_image(prompt="Norwegian fjord, cinematic, golden hour", width=1920, height=1080)
```
### Excluding unwanted elements
> *"Generate a clean product photo of a coffee mug, no background clutter, no text"*
```
→ generate_image(
prompt="product photo of a ceramic coffee mug, studio lighting, white background",
negative_prompt="clutter, text, watermark, blurry, shadows"
)
```
### More inference steps for higher quality
> *"Generate a highly detailed oil painting of a medieval castle, use 20 steps"*
```
→ generate_image(
prompt="oil painting of a medieval castle, highly detailed, dramatic lighting",
steps=20,
model="flux1-dev.safetensors" # FLUX.1-dev supports higher step counts better
)
```
### Check what models are available
> *"List what models are available in ComfyUI"*
```
→ list_available_models()
```
### Check status of a long-running job
> *"What's the status of prompt ID abc-123?"*
```
→ get_generation_status(prompt_id="abc-123")
```
### Find out where images are saved
> *"Where are my generated images being saved?"*
```
→ get_output_directory()
```
---
## 4. Available Tools
### `generate_image`
Generate an image from a text prompt using ComfyUI's FLUX.1-schnell workflow.
**Full signature:**
```python
async def generate_image(
prompt: str,
width: int = 1024,
height: int = 1024,
steps: int = 4,
model: str = "flux1-schnell.safetensors",
seed: int = -1,
negative_prompt: str = "",
output_dir: str = "",
) -> list[TextContent | ImageContent]
```
**What it does:**
1. Loads the bundled `flux_schnell.json` ComfyUI API workflow template
2. Injects your prompt, dimensions, seed, model into the correct workflow nodes
3. Submits the workflow to ComfyUI via `POST /api/prompt`
4. Polls `/api/queue` every 2 seconds until the job leaves the queue
5. Fetches history via `/api/history/{prompt_id}` to find the output filename
6. Downloads the PNG from `/api/view`
7. Saves the PNG to disk as `YYYYMMDD_HHMMSS_{seed}.png`
8. Returns `[TextContent(path + metadata), ImageContent(base64 PNG)]`
---
### `list_available_models`
List all checkpoint models currently available in ComfyUI.
```python
async def list_available_models() -> list[str]
```
Calls `/object_info/CheckpointLoaderSimple` and extracts the checkpoint name list. Use this to discover what models are installed before passing a `model` name to `generate_image`.
**Example return:**
```json
["flux1-schnell.safetensors", "flux1-dev.safetensors", "sd_xl_base_1.0.safetensors"]
```
---
### `get_generation_status`
Check the status of a queued or running generation job.
```python
async def get_generation_status(prompt_id: str) -> dict
```
**Return values:**
| `status` | Meaning |
|---|---|
| `"pending"` | Job is in the queue, not yet started |
| `"running"` | Job is currently being processed |
| `"completed"` | Job finished — image is in ComfyUI's history |
| `"not_found"` | Unknown prompt_id — may have expired from history |
| `"error"` | ComfyUI was unreachable |
Useful when `generate_image` times out (default 120s) — the job may still be running in ComfyUI.
---
### `get_output_directory`
Return the absolute path where generated images will be saved.
```python
def get_output_directory() -> str
```
Returns the expanded, absolute path derived from `IMAGE_OUTPUT_DIR` env var (or `~/Pictures/mcp-generated` default). The directory may not exist yet — `generate_image` creates it on first use.
---
## 5. Parameters Reference
Full parameter table for `generate_image`:
| Parameter | Type | Default | Description |
|---|---|---|---|
| `prompt` | `str` | *(required)* | Text description of the image. Goes into the positive CLIP text encoder node. |
| `width` | `int` | `1024` | Image width in pixels. FLUX.1-schnell: 5122048 recommended. |
| `height` | `int` | `1024` | Image height in pixels. FLUX.1-schnell: 5122048 recommended. |
| `steps` | `int` | `4` | Number of KSampler inference steps. FLUX.1-schnell is designed for 18 steps. |
| `model` | `str` | `"flux1-schnell.safetensors"` | Checkpoint model filename as listed by `list_available_models`. |
| `seed` | `int` | `-1` | RNG seed for reproducibility. `-1` = new random seed each call (0 to 2³²−1). |
| `negative_prompt` | `str` | `""` | Text description of things to exclude. Goes into negative CLIP encoder node. |
| `output_dir` | `str` | `""` | Override save directory. Empty = uses `IMAGE_OUTPUT_DIR` env var or default. |
### Recommended dimensions
| Use case | Width | Height |
|---|---|---|
| Square (default) | 1024 | 1024 |
| Portrait | 768 | 1024 |
| Landscape | 1024 | 768 |
| Widescreen | 1280 | 720 |
| HD widescreen | 1920 | 1080 |
| Tall portrait | 512 | 768 |
> **VRAM note:** Patrick's RX 7900 XTX has 24GB VRAM. FLUX.1-schnell requires ~8GB,
> so you can comfortably run 1920×1080 and even larger. FLUX.1-dev requires ~12GB.
---
## 6. Output Format
`generate_image` returns a list with **two items** when successful:
### Item 1 — `TextContent` (file path + metadata)
```
Generated: /home/pplate/Pictures/mcp-generated/20260404_121500_3847291045.png
Seed: 3847291045
Elapsed: 8.3s
Size: 1024x1024, Steps: 4, Model: flux1-schnell.safetensors
```
The filename format is `YYYYMMDD_HHMMSS_{seed}.png` — the seed is embedded so you can reproduce the exact image by passing it back as the `seed` parameter.
### Item 2 — `ImageContent` (inline base64 PNG)
The image displays **directly in Roo Code / Claude Desktop chat** as an inline image — no need to open a file browser. The same PNG is also saved to disk at the path shown in the TextContent.
```json
{
"type": "image",
"mimeType": "image/png",
"data": "<base64-encoded PNG bytes>"
}
```
### Error responses
When ComfyUI is unreachable or an error occurs, only **one** `TextContent` is returned (no ImageContent):
```
ComfyUI not reachable at http://localhost:8188. Start it with: python main.py --listen
```
```
Generation timed out after 120s. prompt_id=abc-123 — use get_generation_status to check
```
---
## 7. Environment Variables
Configure via environment variables in [`.roo/mcp.json`](../../.roo/mcp.json) or shell:
| Variable | Default | Description |
|---|---|---|
| `COMFYUI_URL` | `http://localhost:8188` | Base URL of the running ComfyUI REST API. Change this if ComfyUI runs on a different host or port. |
| `IMAGE_OUTPUT_DIR` | `~/Pictures/mcp-generated` | Directory where generated PNG files are saved. Supports `~` expansion. Created automatically on first generation. |
| `COMFYUI_TIMEOUT` | `120` | Maximum seconds to wait for a generation job before returning a timeout error. Increase for very large images or slow hardware. |
### Setting via shell
```bash
export COMFYUI_URL="http://localhost:8188"
export IMAGE_OUTPUT_DIR="/home/pplate/Pictures/ai-art"
export COMFYUI_TIMEOUT="300"
./run.sh
```
### Setting via mcp.json env block
```json
"mcp-image-gen": {
"command": "uv",
"args": ["--directory", "/home/pplate/pi_mcps/mcp/mcp-image-gen", "run", "src/server.py"],
"env": {
"COMFYUI_URL": "http://localhost:8188",
"IMAGE_OUTPUT_DIR": "/home/pplate/Pictures/mcp-generated",
"COMFYUI_TIMEOUT": "120"
}
}
```
---
## 8. Test Status
**19 pytest tests — all passing.** Tests mock all ComfyUI HTTP calls using [respx](https://lundberg.github.io/respx/). No running ComfyUI instance is needed to run the tests.
```bash
cd /home/pplate/pi_mcps/mcp/mcp-image-gen
uv run pytest tests/ -v
```
### Test coverage breakdown
| Test file | Tests | Coverage area |
|---|---|---|
| [`tests/test_server.py`](tests/test_server.py) | 19 | All 4 tools + workflow builder |
| Test name | What it verifies |
|---|---|
| `test_build_flux_workflow_structure` | Workflow has correct node class_types |
| `test_build_flux_workflow_params_injected` | All params injected into correct nodes |
| `test_negative_prompt_included` | Negative prompt goes to node 33 |
| `test_random_seed_generated` | `seed=-1` produces a valid integer in `_meta` |
| `test_list_available_models` | Returns model list from mocked `/object_info` |
| `test_list_available_models_comfyui_offline` | ConnectError → graceful error string |
| `test_get_generation_status_pending` | `prompt_id` in queue_pending → `"pending"` |
| `test_get_generation_status_running` | `prompt_id` in queue_running → `"running"` |
| `test_get_generation_status_complete` | Not in queue + in history → `"completed"` |
| `test_get_output_directory_default` | No env var → `~/Pictures/mcp-generated` expanded |
| `test_get_output_directory_custom` | Custom env var → that path returned |
| `test_generate_image_success` | Full lifecycle: queue→poll→history→view→save |
| `test_generate_image_comfyui_unavailable` | ConnectError → single TextContent error |
| `test_generate_image_timeout` | COMFYUI_TIMEOUT=0 → timeout TextContent |
| `test_generate_image_empty_prompt` | Empty string prompt → still succeeds |
| `test_generate_image_long_prompt` | 500-char prompt → not truncated, succeeds |
| `test_generate_image_invalid_model` | 404 from /prompt → error TextContent, no file saved |
| `test_generate_image_custom_output_dir` | Custom `output_dir` param → saved there, dir created |
| `test_generate_image_random_seed_variance` | `seed=-1` × 2 → different seeds, different filenames |
### Test mock stack
- **[respx](https://lundberg.github.io/respx/)** — HTTP-level mocking for all ComfyUI API endpoints
- **[Pillow](https://pillow.readthedocs.io/)** (in conftest) — generates real PNG bytes for image response fixtures
- **monkeypatch** — env vars (`IMAGE_OUTPUT_DIR`, `COMFYUI_URL`, `COMFYUI_TIMEOUT`) and server module attributes
Real image generation requires ComfyUI to be running. Tests prove the tool logic is correct at the protocol level.
---
## 9. Prompt Tips for FLUX.1-schnell
FLUX.1-schnell is a guidance-distilled model designed for speed at 18 steps. It responds differently from SDXL or SD1.5.
### Prompt structure that works well
```
[subject], [style/medium], [lighting], [camera/composition], [mood/atmosphere], [quality modifiers]
```
**Example:**
```
ancient library at night, oil painting, warm candlelight, wide angle, mysterious atmosphere, highly detailed, sharp focus
```
### Style keywords
| Style | Prompt keywords |
|---|---|
| Photography | `cinematic photograph, DSLR, 85mm lens, shallow depth of field, bokeh` |
| Oil painting | `oil painting, thick brushstrokes, textured canvas, impressionist` |
| Watercolor | `watercolor painting, soft washes, paper texture, flowing colors` |
| Digital art | `digital art, concept art, artstation, octane render` |
| Anime/illustration | `anime style, cel shading, vibrant colors, clean linework` |
| Sketch | `pencil sketch, hand drawn, crosshatching, charcoal` |
### Lighting keywords
- `golden hour`, `blue hour`, `dramatic lighting`, `rim lighting`
- `studio lighting`, `soft diffused light`, `volumetric light`
- `neon glow`, `bioluminescent`, `moonlit`, `candlelight`
### What works well with FLUX.1-schnell
- **Clear subject + style** — "red panda in a cozy library, watercolor style"
- **Landscape scenes** — fjords, forests, cities, abstract environments
- **Portrait shots** — animals and characters with descriptive appearance
- **Concept art** — futuristic cities, sci-fi environments, fantasy scenes
- **Low step counts** — 4 steps is designed to be near-optimal for this model
### What to avoid
- **Booru-style tag dumps** (FLUX handles natural language better than SD1.5)
- **Contradictory instructions** — "dark AND bright", "realistic AND cartoon"
- **Overly complex scenes** at very small resolutions
### Using the negative prompt
FLUX.1-schnell has reduced CFG guidance so negative prompts have less impact than in SDXL.
Use them for broad exclusions:
```
negative_prompt="blurry, out of focus, watermark, text, signature, low quality, artifacts"
```
### Reproducibility
Always save the seed from the TextContent output if you want to reproduce a result:
```
Seed: 3847291045
```
Then pass it back: `seed=3847291045`
---
## 10. Known Limitations
### ComfyUI must run locally
The MCP server connects to `COMFYUI_URL` (default: `http://localhost:8188`). ComfyUI is a local application — it does not have a cloud API. You must start it before requesting image generation. The server returns a clear error message if ComfyUI is not reachable.
### Model must be pre-loaded
ComfyUI loads checkpoint models into VRAM on first use. The first generation with a model takes longer as VRAM is allocated (FLUX.1-schnell: ~8GB). Subsequent generations with the same model are faster.
```bash
# Verify model is installed before generation
# → ask Lumen: "list available models in ComfyUI"
```
### AMD ROCm setup complexity
AMD GPU support requires:
1. ROCm drivers installed (`rocm-smi` working)
2. PyTorch built with ROCm support (not the default CUDA build)
3. `HSA_OVERRIDE_GFX_VERSION=11.0.0` for RX 7900 XTX (gfx1100)
Without these, ComfyUI will fall back to CPU — very slow (minutes per image vs. ~8 seconds on RX 7900 XTX).
Check GPU is being used:
```bash
# In another terminal while generating:
watch -n 1 rocm-smi
# VRAM usage should spike to ~8GB during generation
```
### Timeout on large images
The default `COMFYUI_TIMEOUT=120` (2 minutes) may not be enough for:
- Very large resolutions (2048×2048+)
- High step counts (20+)
- First generation loading a new model
Increase via env var:
```bash
export COMFYUI_TIMEOUT=300 # 5 minutes
```
If `generate_image` returns a timeout error, the job may still be running in ComfyUI. Use `get_generation_status(prompt_id)` to check.
### Ollama image gen is macOS-only (April 2026)
Ollama launched experimental image generation in January 2026, but it is **macOS-only** as of April 2026. Linux support is announced as "coming soon." When Linux support arrives, the server can switch backends via `IMAGE_BACKEND=ollama` without changing any tool signatures.
### ComfyUI history is ephemeral
ComfyUI keeps generation history in memory — it is lost on restart. The `get_generation_status` tool will return `"not_found"` for old prompt IDs after a ComfyUI restart. The saved PNG file on disk persists regardless.
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[Unit]
Description=ComfyUI — Local AI Image Generation (AMD ROCm / FLUX.1-schnell)
Documentation=https://github.com/comfyanonymous/ComfyUI
After=network.target
[Service]
Type=simple
WorkingDirectory=%h/ComfyUI
ExecStart=%h/ComfyUI/.venv/bin/python main.py --listen --port 8188
Restart=on-failure
RestartSec=10
# AMD RX 7900 XTX ROCm GFX override — required for correct GPU detection
Environment=HSA_OVERRIDE_GFX_VERSION=11.0.0
# Redirect output — follow with: journalctl --user -u comfyui -f
StandardOutput=journal
StandardError=journal
[Install]
WantedBy=default.target
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