Context Bridge — operating doc

See PLAN.md for the original design rationale. This file is the practical "how do I actually run this" companion.

Installation

bash install.sh

The wizard creates a .venv, installs dependencies, registers the MCP server with Claude Code (global by default, so it's available in every session), and writes a .env file for local config.

Configuration — .env (created by the wizard, gitignored) supports:

Edit .env directly to change these after initial setup. See .env.example for the template.

Related MCP server: claude-kb

Getting your Claude.ai export

There is no API for this — the export is pull-only, triggered manually:

1. Go to Claude.ai → Settings → Account → Export Data

2. Anthropic emails you a .dms file attachment (has been a few minutes in my exp)

3. Run ./build_all.sh path/to/export.dms — it handles the rename, unpack, and rebuild

Abstract overview

Claude.ai export (.zip)                ~/.claude/projects/**/*.jsonl
      │  unzip                                │  ingest_code_sessions.py
      ▼                                       │  (incremental, walk parentUuid tree)
data/inspect/                                 │
      │  ingest.py + embed.py                 │
      │  (full rebuild via build_db.py)       │
      └──────────────────┬────────────────────┘
                         ▼
               chat_memory.db   (SQLite — chunks + sessions + meta)
                         │  source: 'claude_ai' | 'claude_code'
                         │  server.py: search_chat_history, get_conversation
                         ▼
               Claude Code session, via "context-bridge" MCP

build_db.py always parses the entire data/inspect/ export and re-embeds everything, but before the atomic replace it merges back any claude_ai chunks from the previous DB whose conversation/project UUID is absent from the new export. This means a partial export (e.g. 90-day-only) is safe — older history that isn't in the new export is preserved from the old DB.

One exception: if the embedding model changes between builds, the merge is skipped (mixing vectors from two models would corrupt search). In that case run build_db.py with a full export to get a clean rebuild.

Command cheat sheet

Refresh the DB from a new export (standard workflow — run this whenever you pull a new Claude.ai export):

cd context_bridge
./build_all.sh data/chat-archive-<date>.dms

Pass the export file (.dms or .zip) and build_all.sh handles the unpack step automatically. Omit the argument if data/inspect/ is already populated.

Run the MCP server manually (for a quick smoke check outside Claude Code):

cd context_bridge
./run_server.sh

(This is also what install.sh registers as the context-bridge MCP server — no separate setup needed once the venv/deps exist.)

Sanity-check ingest/parsing only (no embedding, no DB write — just see what the export produces):

cd context_bridge
python3 ingest.py            # defaults to ./data/inspect

Run the retrieval/smoke tests:

cd context_bridge
python3 smoke_test.py
python3 retrieval_smoke_test.py
python3 mcp_smoke_test.py

Refresh the DB from new Claude Code sessions (incremental — safe to re-run any time; skips already-ingested sessions):

cd context_bridge
python3 ingest_code_sessions.py

How the MCP server is actually used

The server registers two tools with Claude at session start: search_chat_history and get_conversation. Their schemas cost ~100–200 tokens each for the lifetime of the session, whether or not they're ever called.

What triggers a search: the tool description drives autonomous behavior. The current description is reactive — Claude calls search_chat_history when it notices it's about to re-derive something it suspects has been covered before. In a narrow coding task it may never fire; in a design or planning conversation it may fire more.

The most reliable pattern: ask explicitly.

"Search the context bridge for [topic]."

This produces a direct, well-formed tool call rather than leaving query construction to Claude's autonomous judgment. Semantic search rewards descriptive phrases over single keywords — "what did we decide about chunking strategy" retrieves better than "chunking".

Current retrieval limitation: search_chat_history has no locality signal. A query from a sol_reason session ranks sol_reason sessions no higher than sessions from synesthesia, djmgmt, or any other project. This is the Phase 3 gap (current_project parameter — see PLAN.md). Until Phase 3 is implemented, cross-project noise is a known retrieval quality ceiling.

Notes / known constraints

• Export is manual, pull-only (Claude.ai Settings → Account → Export Data) — no API/webhook trigger.

• build_db.py writes to a .tmp file and os.replaces it into place, so a crash mid-rebuild never leaves a half-written chat_memory.db live.

• chat_memory.db and data/ are gitignored — they're local build artifacts and data files, not committed.