book-notes-mcp is a personal reading system for turning messy capture into usable notes.

The input can be rough: something typed quickly on your phone, an OCR scrape from a page, a voice transcript, or a note pulled in from Google Keep. The app processes that raw text once, stores the result in Supabase, generates embeddings, and makes it available through semantic search, RAG-style Q&A, and a small web interface.

It is built for one practical problem: reading produces a lot of fragments, but the value only shows up later when those fragments are easy to retrieve and connect.

• Processes raw notes into a structured shape: book_title, summary, ideas, tags, actions
• Stores both the raw capture and the processed note
• Builds embeddings for retrieval across your reading history
• Exposes the knowledge base through MCP tools, a web API, and a browser UI
• Syncs notes from Google Keep using gkeepapi

1. Capture notes wherever it is easiest. Google Keep is the intended inbox for phone capture because it handles typing, OCR, and voice well.
2. Let this project do the organizing. Raw text is processed into something cleaner and easier to search.
3. Retrieve later by concept, not by memory of exact wording. That is what the embeddings and Q&A layer are for.

• FastAPI web app for the browser UI and HTTP endpoints
• Supabase for note storage and vector search
• LiteLLM for model routing
• MCP server for Claude Desktop / Cursor workflows
• gkeepapi for scheduled Google Keep sync

git clone <your-repo>
cd book-notes-mcp
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

At minimum:

GEMINI_API_KEY=<your key>
SUPABASE_URL=<your project url>
SUPABASE_SERVICE_KEY=<your service role key>

Optional model configuration:

LLM_MODEL=gemini/gemini-2.5-flash
EMBEDDING_MODEL=gemini/text-embedding-004
VECTOR_DIM=768

Optional Keep sync configuration:

GOOGLE_EMAIL=<your google account email>
GOOGLE_APP_PASSWORD=<16-character app password>
KEEP_LABEL=book-note
KEEP_TOKEN_FILE=.keep_token
KEEP_SYNC_INTERVAL_MINUTES=5

Run schema.sql in the Supabase SQL editor.

Note on vector size:

• Gemini text-embedding-004: use vector(768)
• OpenAI text-embedding-3-small: use vector(1536)
• If you change embedding dimensions, update both the schema and VECTOR_DIM

Add this to Claude Desktop:

{
  "mcpServers": {
    "book-notes": {
      "command": "/path/to/book-notes-mcp/.venv/bin/python",
      "args": ["/path/to/book-notes-mcp/book_server/server.py"],
      "env": {
        "PYTHONPATH": "/path/to/book-notes-mcp"
      }
    }
  }
}

Start the web app locally:

python web/app.py

Open:

• http://localhost:8080 on the same machine
• http://<your-local-ip>:8080 from your phone on the same network

The intended setup is a dedicated Google account used only for capture. The server polls that account through gkeepapi and imports notes matching the configured Keep label.

1. Turn on 2-step verification for the Google account
2. Create a Google App Password
3. Put GOOGLE_EMAIL and GOOGLE_APP_PASSWORD in .env
4. Optionally set KEEP_LABEL if you do not want to use the default book-note

The first successful sync caches a Keep token in KEEP_TOKEN_FILE.

book-notes-keep-sync

The sync behavior is:

• new Keep note with the configured label -> create a new note in the library
• changed Keep note -> update the existing mapped note and refresh its embedding
• unchanged Keep note -> skip
• deleted or missing Keep note -> no automatic deletion in the app

Run the sync command every 5 minutes on the deployed server.

Example cron entry:

*/5 * * * * cd /path/to/book-notes-mcp && /path/to/.venv/bin/book-notes-keep-sync >> keep-sync.log 2>&1

Web endpoints in web/app.py:

• POST /api/process
• POST /api/ask
• POST /api/search
• GET /api/notes
• GET /api/notes/{id}
• GET /api/notes/{id}/link
• POST /api/webhook/keep

Run tests:

pytest tests/ -v

The current test suite focuses on processing logic and Keep sync behavior with mocked external services.

book-notes-mcp/
├── agent/                  RAG query logic
├── book_server/            MCP server, Keep sync, ingestion services
├── embeddings/             embedding generation and storage helpers
├── processing/             note extraction and summarization
├── storage/                Supabase and filesystem persistence
├── tests/                  unit tests
├── web/                    FastAPI app and frontend
├── config.py               environment-driven config
├── pyproject.toml          package metadata and scripts
└── schema.sql              database schema

• Delete propagation from Keep into the library
• Book-level rollups
• Graph-style visualization
• A polished mobile capture app of its own

That is deliberate. The project is currently optimized around capture, storage, and retrieval rather than around building a full reading platform.