How do I use ragwatcher?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@ragwatcher query my notes directory for what I wrote about RAG indexing" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

ragwatcher

Local RAG over a directory. Incremental. Served as MCP tool + CLI.

Zero-config default (drops into ~/notes, works).
Offline, CPU-only baseline (FastEmbed ONNX). GPU / API models opt-in.
CLI is a peer to the MCP server, not a wrapper.

Install

uv tool install ragwatcher
# or
pipx install ragwatcher

Optional extras: [lance] (LanceDB store), [pdf-pro] (pymupdf), [remote] (qdrant/openai/cohere).

Related MCP server: Corpus-KB

Usage

ragwatcher serve ~/notes                  # MCP server (stdio)
ragwatcher index ~/notes                  # one-shot sync
ragwatcher query ~/notes "what is X"      # one-shot query
ragwatcher stats ~/notes                  # index summary
ragwatcher doctor ~/notes                 # health check
ragwatcher purge ~/notes --yes            # wipe .rag_index/

Every data-producing command supports --json for scripting.

Configuration

Precedence (low → high): defaults → $XDG_CONFIG_HOME/ragwatcher/config.toml → <DIR>/.ragwatcher.toml → RAGWATCHER_* env vars → CLI flags.

See SPEC.md § 5 for the full schema.

Development

uv sync --extra dev
uv run pytest -q --ignore=tests/integration
uv run ruff check src tests
uv run mypy src

Integration tests (real embed model, ~3s):

uv run pytest tests/integration -m slow

License

MIT

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityMaintained

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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