galaxy-mcp 0.1.0

Model Context Protocol server for Galaxy bioinformatics platform

Galaxy MCP Server - Python Implementation

This is the Python implementation of the Galaxy MCP server, providing a Model Context Protocol server for interacting with Galaxy instances.

Features

• Complete Galaxy API integration through BioBlend
• Interactive Workflow Composer (IWC) integration
• FastMCP server for high-performance operation
• Type-annotated Python codebase

Requirements

• Python 3.12+
• BioBlend 1.5.0+
• MCP SDK 1.6.0+
• Requests 2.32.3+

Installation

From PyPI (Recommended)

# Install from PyPI
pip install galaxy-mcp

# Or using uv package manager
uv pip install galaxy-mcp

From Source

# Clone the repository
git clone https://github.com/galaxyproject/galaxy-mcp.git
cd galaxy-mcp/mcp-server-galaxy-py

# Install in development mode
pip install -e .

# Or using uv package manager
uv pip install -e .

Configuration

The server requires Galaxy credentials to connect to an instance. You can provide these via environment variables:

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

Alternatively, create a .env file in the project root with these variables.

Usage

As a standalone MCP server

# Run the MCP server
galaxy-mcp

# The server will wait for MCP protocol messages on stdin

With MCP clients

# Use with MCP CLI tools
mcp connect galaxy-mcp

# Use with MCP developer tools
mcp dev galaxy-mcp

# Use with other MCP-compatible clients
your-mcp-client galaxy-mcp

See USAGE_EXAMPLES.md for detailed usage patterns and common examples.

Available MCP Tools

The Python implementation provides the following MCP tools:

• connect: Establish connection to a Galaxy instance
• search_tools: Find Galaxy tools by name
• get_tool_details: Retrieve detailed tool information
• run_tool: Execute a Galaxy tool with parameters
• get_tool_panel: Retrieve the Galaxy tool panel structure
• get_user: Get current user information
• get_histories: List available Galaxy histories
• list_history_ids: Get simplified list of history IDs and names
• get_history_details: Get detailed information about a specific history
• upload_file: Upload local files to Galaxy
• get_invocations: View workflow executions
• get_iwc_workflows: Access Interactive Workflow Composer workflows
• search_iwc_workflows: Search IWC workflows by keywords
• import_workflow_from_iwc: Import an IWC workflow to Galaxy

Testing

The project includes a comprehensive test suite using pytest with mock-based testing.

Running Tests

# Install test dependencies
uv pip install -r requirements-test.txt

# Run all tests
uv run pytest

# Run with coverage report
uv run pytest --cov=main --cov-report=html

# Run specific test file
uv run pytest tests/test_history_operations.py

# Run tests with verbose output
uv run pytest -v

Test Structure

Tests are organized by functionality:

• test_connection.py - Galaxy connection and authentication
• test_history_operations.py - History-related operations
• test_dataset_operations.py - Dataset upload/download
• test_tool_operations.py - Tool search and execution
• test_workflow_operations.py - Workflow import and invocation
• test_integration.py - End-to-end scenarios

See tests/README.md for more details on the testing strategy.

Development

Code Style Guidelines

• Use Python 3.12+ features
• Employ type hints where appropriate
• Follow PEP 8 style guidelines

Development Commands

• Lint the code: ruff check .
• Format the code: ruff format .
• Start the development server: mcp dev main.py

License

MIT