Patrick Plate
8f24168dcd
conversation_chunks_fts is a standalone FTS5 table (no content= option).
The old INSERT ... VALUES('rebuild') is a no-op on standalone tables and
left deleted chunks searchable in the FTS shadow tables.
Fix: collect IDs before deletion, explicitly DELETE FROM conversation_chunks_fts
WHERE rowid IN (...) before removing from the main table. This keeps FTS
in sync after every vacuum call.
Tests: 303/303 passing. Vacuum tests now pass for the right reason.
mcp-adp-bigmind — BigMind Memory
Persistent memory for AI conversations — from a single laptop to the collective intelligence of your company.
What it does
Every AI conversation normally starts from scratch — no memory of who you are, what you built last week, or decisions you've already made.
BigMind gives GitHub Copilot (and any other MCP-compatible AI) a persistent memory that survives across sessions:
- Tier 0 — Your identity: role, preferences, pinned facts (always loaded, ~150 tokens)
- Tier 1 — Session index: one-liner + topics for each past conversation (always loaded, ~400 tokens)
- Tier 2 — Session detail: rich narrative for a specific past session (on-demand, ~600 tokens)
- Tier 3 — Flagged chunks: verbatim excerpts of important exchanges (on-demand, FTS5-indexed)
Total cold-start overhead: ~550 tokens — invisible in a 128K context window.
Quick start
# 1. Run the main installer from the pi_mcps root (same as all other servers)
cd /path/to/pi_mcps
bash install.sh
# → Select your IDE, then select "mcp-adp-bigmind" from the list
# → It will ask for a workspace path for Copilot instructions
# (press Enter to use the pi_mcps root — recommended)
# → Existing .github/copilot-instructions.md content is NEVER overwritten,
# the BigMind block is safely appended
# 2. Tell BigMind who you are (first time only, in Copilot Chat):
memory_update_profile(
role="Principal Engineer — ADP PI",
preferences="Python, FastMCP pattern, concise answers, code over explanation",
pinned_facts="- Building pi_mcps suite\n- Prefer uv\n- Proxy cert at ~/Library/ADP_Support/adp-trusted-certs.pem"
)
How the AI uses it
The AI is instructed through five independent layers (see PLAN.md § 13):
| Layer | Mechanism | Auto? |
|---|---|---|
| 1 | FastMCP server-level instructions= |
✅ automatic |
| 2 | @mcp.prompt() bigmind_init |
✅ / slash cmd |
| 3 | Tool docstring directives | ✅ automatic |
| 4 | .github/copilot-instructions.md |
✅ written by installer |
| 5 | memory_get_instructions tool |
on demand |
Available MCP tools
Session lifecycle
| Tool | When to call |
|---|---|
memory_start_session |
First thing in every conversation |
memory_end_session |
Last thing before closing |
memory_flag_important |
Whenever a decision / code / preference is shared |
Recall
| Tool | Purpose |
|---|---|
memory_get_context |
Refresh context mid-conversation (no side-effects) |
memory_get_session_detail |
Get full Tier-2 narrative for a past session |
memory_search_chunks |
FTS keyword search over flagged Tier-3 chunks |
memory_list_sessions |
Browse past sessions with optional topic filter |
Writing
| Tool | Purpose |
|---|---|
memory_update_profile |
Set/update your identity profile |
memory_store_fact |
Store an atomic fact (preference, decision, codebase note) |
memory_append_chunk |
Manually save an important exchange to Tier 3 |
Utility
| Tool | Purpose |
|---|---|
memory_get_stats |
DB size, session count, facts, chunks |
memory_vacuum |
Prune old Tier-3 chunks (keeps all summaries) |
memory_get_instructions |
Recover usage instructions at any time |
Configuration
| Env var | Default | Description |
|---|---|---|
BIGMIND_USER |
$USER |
Username for multi-user mode |
BIGMIND_DB_PATH |
~/.mcp/bigmind/memory.db |
Path to the SQLite database file |
Database location
~/.mcp/bigmind/memory.db ← personal mode (default)
The file is fully local and never uploaded anywhere.
Development
# Install dependencies
cd mcp-adp-bigmind
uv sync
# Run tests
uv run pytest -v
# Run the server directly
uv run src/server.py
Roadmap
| Phase | Status | Description |
|---|---|---|
| 1 — Personal MVP | ✅ Done | SQLite, all tiers, Copilot instructions |
| 2 — Search & Recall | ✅ Done | FTS search (memory_search_chunks), session filters, vacuum |
| 3 — BigMind Company Brain | 🔜 | Multi-user, Tier G global knowledge, PostgreSQL |
| 4 — Semantic Search | 🔜 | sqlite-vec embeddings, similarity search |
See PLAN.md for full architectural details.