Galaxy MCP Server

This project provides a Model Context Protocol (MCP) server for interacting with the Galaxy bioinformatics platform. It enables AI assistants and other clients to connect to Galaxy instances, search and execute tools, manage workflows, and access other features of the Galaxy ecosystem.

Project Overview

The repository holds two independent implementations of the same Galaxy operation set, and the contract that keeps them in step:

• python/ -- the Python MCP server, published to PyPI as galaxy-mcp. It reaches Galaxy through BioBlend. See the Python README.
• typescript/ -- a pnpm workspace published to npm as @galaxyproject/galaxy-ops, galaxy-cli and galaxy-mcp: one framework-free core (galaxy-ops) with a command line and a stdio MCP server on top. See the TypeScript README.
• contract/ -- what both are held to: the Python surface manifest, golden results replayed through both, the snapshot of Galaxy's built-in server, and the registry of known differences. See the contract README.

The two are meant to stay in step: an operation keeps its name and its meaning across both. They are still separate codebases with separate release trains, though, so each README lists the operations that surface actually has -- read the one you are using.

ROADMAP.md explains why there are two implementations, how they relate to Galaxy's built-in MCP server, and what comes next. PARITY.md is the generated, CI-checked record of where they differ.

Key Features

• Galaxy Connection: Connect to any Galaxy instance with a URL and API key
• OAuth Login (optional): Offer browser-based sign-in that exchanges credentials for temporary Galaxy API keys
• Server Information: Retrieve comprehensive server details including version, configuration, and capabilities
• Tools Management: Search the tool catalog, inspect a tool's inputs, and execute Galaxy tools
• User-Defined Tools: Create, list, run, and deactivate unprivileged user-defined tools
• Workflow Integration: Find and import workflows from the Intergalactic Workflow Commission (IWC), then invoke them and follow their invocations
• History Operations: Manage Galaxy histories, datasets, and collections, and inspect the jobs behind them
• File Management: Upload files to Galaxy from local storage or from a URL, and download results back
• Pages: Read and write Galaxy-flavored markdown pages -- history-attached notebooks and standalone reports -- including their revision history
• Verified Container Recommendation (optional): Resolve a real quay.io/biocontainers image for a set of conda packages instead of guessing one -- see Optional extras
• Mock-based test suite: The default suite runs entirely against mocked Galaxy responses, so it needs no server; a separate suite against a live Galaxy is opt-in and skips when no credentials are configured

Optional extras

container-recommend

Enables the recommend_biocontainer tool, which resolves a verified quay.io/biocontainers image for a set of conda packages. Authoring a user-defined tool means naming a container, and a hallucinated image tag is the most common way a UDT passes validation and then dies at run time with manifest unknown. This wraps galaxy.tool_util.deps.mulled.recommend -- the same resolver Galaxy's own custom-tool agent uses (added in Galaxy 26.1, galaxyproject/galaxy#22981) -- so the image is checked against quay.io rather than invented.

It is an extra rather than a hard dependency because galaxy-tool-util pulls in lxml and conda-package-streaming, and every other tool on this server works without it. The tool is only registered when the extra is installed, so a stock install simply doesn't advertise it.

uvx --from 'galaxy-mcp[container-recommend]' galaxy-mcp
# or, for a local checkout:
cd python && uv sync --extra container-recommend

The same resolver is also available as a standalone CLI once installed: mulled-recommend samtools=1.17.

code-mode

Collapses the whole tool catalog into three meta-tools -- search, get_schema, and run_galaxy_tool -- so an agent pays for a tool's schema only when it decides to use it. The extra pulls in pydantic-monty, the sandboxed interpreter that runs the submitted code. Without the extra the server still starts, but asking for code mode is an error rather than a silent downgrade.

uvx --from 'galaxy-mcp[code-mode]' galaxy-mcp --discovery-mode code

See Tool discovery mode in the Python README for what the trade-off buys you.

Quick Start

The galaxy-mcp CLI ships with both stdio (local) and HTTP transports. Choose the setup that matches your client:

# Stdio transport (default) – great for local development tools
uvx galaxy-mcp

# HTTP transport with OAuth (for remote/browser clients)
# Generate the session secret ONCE (`openssl rand -hex 32`) and store it. Every
# restart and every replica must use the same value, or tokens issued earlier --
# or by another replica -- stop decrypting.
export GALAXY_URL="https://usegalaxy.org.au/"          # Target Galaxy instance
export GALAXY_MCP_PUBLIC_URL="https://mcp.example.com" # Public base URL for OAuth redirects
export GALAXY_MCP_SESSION_SECRET="<your stored secret>"
uvx galaxy-mcp --transport streamable-http --host 0.0.0.0 --port 8000

When running over stdio you can provide long-lived credentials via environment variables:

export GALAXY_URL="https://usegalaxy.org/"
export GALAXY_API_KEY="your-api-key"

For OAuth flows the server exchanges user credentials for short-lived Galaxy API keys on demand, so you typically leave GALAXY_API_KEY unset.

For non-OAuth HTTP clients, connect(url=..., api_key=...) stores Galaxy credentials per MCP session rather than globally, for a URL the operator allowed (GALAXY_URL or GALAXY_MCP_EXTRA_ALLOWED_URLS). That keeps sessions apart but is not authentication: the HTTP transports bind to 127.0.0.1 by default, refuse to serve a non-loopback address without OAuth unless you pass --allow-unauthenticated, and disable the tools that touch the server's filesystem. See Serving over HTTP.

Alternative Installation

# Install from PyPI
pip install galaxy-mcp

# Run (stdio by default)
galaxy-mcp

# Or from source using uv
cd python
uv sync
uv run galaxy-mcp --transport streamable-http --port 8000

Container Usage

Images are published to the GitHub Container Registry as ghcr.io/galaxyproject/galaxy-mcp. Use :latest (default) or pin a release, e.g. :1.10.0.

The published image defaults to stdio transport (no HTTP listener):

docker run --rm -it \
  -e GALAXY_URL="https://usegalaxy.org/" \
  -e GALAXY_API_KEY="your-api-key" \
  ghcr.io/galaxyproject/galaxy-mcp

For OAuth + HTTP:

# Generate once and keep it -- do NOT inline `openssl rand` here, or each
# container start mints a key that invalidates every token issued before it.
export GALAXY_MCP_SESSION_SECRET="<your stored secret>"

docker run --rm -it -p 8000:8000 \
  -e GALAXY_URL="https://usegalaxy.org.au/" \
  -e GALAXY_MCP_TRANSPORT="streamable-http" \
  -e GALAXY_MCP_PUBLIC_URL="https://mcp.example.com" \
  -e GALAXY_MCP_SESSION_SECRET \
  ghcr.io/galaxyproject/galaxy-mcp

Connect to Claude Desktop

• Ensure that GalaxyMCP runs with uvx galaxy-mcp
• Add export GALAXY_URL=https://usegalaxy.org to your .bashrc (or equiv)
• Download and install claude desktop
• Go to Settings -> Developer -> Edit Config
• Add this to claude_desktop_config.json

{
  "mcpServers": {
    "galaxy-mcp": {
      "command": "uvx",
      "args": ["galaxy-mcp"],
      "env": {
        "GALAXY_URL": "https://usegalaxy.org",
        "GALAXY_API_KEY": "SECRETS"
      }
    }
  }
}

• Under the developer menu, you should now see galaxy-mcp as running (you may need to restart Claude desktop)
• Prompt Claude with "can you connect to galaxy"
• If you have not provided the optional env config you'll be asked for connection details which you can provide like "Use my Galaxy API key: XXXXXXX"
• Talk to Claude to work with your galaxy instance, e.g. "give a summary with my histories"

Development Guidelines

See the Python implementation README for specific instructions and documentation.

License

MIT