GraceDB MCP Server

An open-source Model Context Protocol (MCP) server that exposes LIGO/Virgo/KAGRA gravitational-wave alerts from GraceDB to LLM agents. It bridges the official ligo-gracedb client with the MCP standard so an agent can query, classify, and fetch data for gravitational-wave candidate events.

Intended as the data-ingestion layer for a downstream signal-processing / anomaly-detection pipeline (e.g. isolating astrophysical "chirps" from instrumental glitches).

Tools

⚠️ Important: where strain data actually comes from

GraceDB does not host raw strain data. It stores event artifacts (skymaps, classifications, parameter-estimation results). The public strain time series lives at GWOSC and is fetched by GPS time via gwpy. Public strain is only available for released observing runs — fresh low-latency alerts will not have public strain yet. download_strain_data reflects this: it reads t_0 from GraceDB, then pulls open data from GWOSC (and cleanly reports when data isn't public).

Requirements

• Python 3.10+
• mcp, ligo-gracedb, gwosc, python-dotenv
• Optional: gwpy (for download_strain_data and utc_to_gps) — installed via the strain extra.

Install

Run straight from PyPI with uv:

uvx --from "gracedb-mcp[strain]" gracedb-mcp

Or from source:

git clone https://github.com/izabelamaria24/gracedb-mcp.git
cd gracedb-mcp
python -m venv .venv && source .venv/bin/activate
pip install -e ".[strain]"     # omit [strain] if you don't need strain download

Configuration

Copy .env.example to .env and adjust. Anonymous access is enough for public superevents:

cp .env.example .env

Run locally

gracedb-mcp          # speaks MCP JSON-RPC over stdio

Development

Requires Python 3.11+ (the mcp 2.x SDK needs 3.10+).

pip install -e ".[dev]"
pytest -q            # tests mock the GraceDB client, no network needed

Run with Docker

A prebuilt image (linux/amd64 and arm64, with strain support) is published to GHCR:

docker run --rm -i -v gracedb-data:/data ghcr.io/izabelamaria24/gracedb-mcp:latest

Or build it yourself:

docker build -t gracedb-mcp .
docker run --rm -i -v gracedb-data:/data gracedb-mcp

Connect to an MCP client

Claude Desktop / VS Code (mcp.json)

Add to your MCP client configuration:

{
  "mcpServers": {
    "gracedb": {
      "command": "gracedb-mcp",
      "env": {
        "GRACEDB_FORCE_NOAUTH": "true",
        "GRACEDB_MCP_DATA_DIR": "./data"
      }
    }
  }
}

Or run the container directly:

{
  "mcpServers": {
    "gracedb": {
      "command": "docker",
      "args": ["run", "--rm", "-i", "-v", "gracedb-data:/data", "ghcr.io/izabelamaria24/gracedb-mcp:latest"]
    }
  }
}

Example agent prompts

• "Search for superevents from 2023-05-01 to 2023-06-01 with a FAR below 1e-8."
• "What's the source classification for S230518h? Is it more likely a BBH or an NSBH?"
• "Download the skymap and p_astro file for S230518h."
• "Fetch H1 and L1 strain around GW150914."
• "What are the masses of GW190814, and how certain are they?"
• "List GWTC-3 events with total mass under 10 solar masses."
• "Was S190814bv checked by a human? What do its labels mean?"
• "Is there public strain for GW190814 from every detector?"

RL environment (experimental)

gw_rl is a Gymnasium environment built on strain fetched by this server. Each episode shows the agent a 1 s spectrogram and it answers noise, chirp or glitch (+1 correct, -1 wrong).

Real chirps are scarce, so training injects synthetic chirps into real detector noise; the real catalogue events are kept for evaluation. Chirps are IMRPhenomD waveforms from LALSimulation (--chirp imr, default) or a simple analytic sweep (--chirp toy). Glitches are real LIGO glitches labelled by Gravity Spy (--glitch real, default) or sine-Gaussian bursts (--glitch synthetic).

It is not part of the PyPI package; run it from a clone of this repository (needs pip 25.1+):

pip install -e . --group rl
python scripts/fetch_training_strain.py --catalog GWTC-3-confident  # small files, a few MB
python scripts/fetch_glitches.py --files 30  # ~3 GB of GWOSC hour files, cached by gwpy
python scripts/train_agent.py --algo ppo mlp cnn --verbose

Roadmap

• Streamable-HTTP transport (in addition to stdio) for remote deployments.
• MCP resources for cached skymaps and p_astro files.

Disclaimer

Unofficial; not affiliated with or endorsed by LIGO, Virgo, KAGRA or GraceDB. Respect the GWOSC and GraceDB data-use policies.

If you publish results using data fetched by this server, cite the data sources: GWOSC (CC BY 4.0) and, for glitch labels, Gravity Spy.

License

GPL-3.0-or-later, matching its core dependencies ligo-gracedb and gwpy.