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Galaxy Mcp

@galaxyprojecton a year ago
5 MIT
FreeCommunity
AI Systems
Galaxy MCP Server enables AI and clients to interact with Galaxy for tools and workflows.

Overview

What is Galaxy Mcp

Galaxy MCP is a server that enables AI and clients to interact with the Galaxy bioinformatics platform for tools and workflows.

Use cases

Use cases include integrating AI assistants with Galaxy, automating bioinformatics workflows, and managing datasets and tools in a collaborative research environment.

How to use

To use Galaxy MCP, clone the repository for either the TypeScript or Python implementation, install the necessary dependencies, and run the server. Set up your Galaxy credentials using environment variables.

Key features

Key features include connecting to any Galaxy instance, managing tools and workflows, handling histories and datasets, and uploading files from local storage.

Where to use

Galaxy MCP can be used in bioinformatics, research institutions, and any field that requires data analysis and workflow management through the Galaxy platform.

Content

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

This repository contains a Python-based MCP server implementation that provides comprehensive integration with Galaxy’s API through BioBlend.

Note: There is also a work-in-progress TypeScript implementation available in a separate branch of this repository.

Key Features

  • Galaxy Connection: Connect to any Galaxy instance with a URL and API key
  • Server Information: Retrieve comprehensive server details including version, configuration, and capabilities
  • Tools Management: Search, view details, and execute Galaxy tools
  • Workflow Integration: Access and import workflows from the Interactive Workflow Composer (IWC)
  • History Operations: Manage Galaxy histories and datasets
  • File Management: Upload files to Galaxy from local storage
  • Comprehensive Testing: Full test suite with mock-based testing for reliability

Quick Start

The fastest way to get started is using uvx:

# Run the server directly without installation
uvx galaxy-mcp

# Run with MCP developer tools for interactive exploration
uvx --from galaxy-mcp mcp dev galaxy_mcp.server

# Run as a deployed MCP server
uvx --from galaxy-mcp mcp run galaxy_mcp.server

You’ll need to set up your Galaxy credentials via environment variables:

export GALAXY_URL=<galaxy_url>
export GALAXY_API_KEY=<galaxy_api_key>

Alternative Installation

# Install from PyPI
pip install galaxy-mcp

# Or from source
cd mcp-server-galaxy-py
pip install -r requirements.txt
mcp run main.py

Development Guidelines

See the Python implementation README for specific instructions and documentation.

License

MIT

Tools

No tools

Comments

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