galaxy-mcp 0.2.1

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

Quick Start with uvx

The fastest way to run the Galaxy MCP server 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

As a standalone MCP server

# Install and run the MCP server
pip install galaxy-mcp
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
• Use ruff for code formatting and linting
• All code should pass type checking with mypy

Development Setup

# Install development dependencies
make install-dev

# Set up pre-commit hooks (required for contributing)
uv run pre-commit install

Pre-commit hooks will automatically format your code and run linting checks when you commit. All contributors should install these hooks to maintain consistent code quality.

Development Commands

We use a Makefile for consistent development commands:

# Show all available commands
make help

# Install dependencies
make install        # Install runtime dependencies
make install-dev    # Install development dependencies

# Code quality
make lint          # Run linting checks (via pre-commit)
make format        # Auto-format code (via pre-commit)
make format-quick  # Quick format (ruff + prettier only)
make check         # Run all checks (lint + test)

# Testing
make test          # Run tests
make test-cov      # Run tests with coverage report
make test-watch    # Run tests in watch mode

# Building
make clean         # Clean build artifacts
make build         # Build distribution packages

# Running
make run           # Run the MCP server
make dev           # Run MCP dev server with explorer

Using uv directly

All commands can also be run directly with uv:

# Lint the code
uv run ruff check .

# Format the code
uv run ruff format .

# Run tests
uv run pytest

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

Cross-version Testing

Test across multiple Python versions using tox:

# Test on all supported Python versions
tox

# Test on specific version
tox -e py312

# Run only linting
tox -e lint

# Run type checking
tox -e type

Pre-commit Hooks

The project uses pre-commit hooks for automatic code quality checks:

# Install pre-commit hooks (one-time setup)
uv run pre-commit install

# Run pre-commit manually on all files
uv run pre-commit run --all-files

# Skip pre-commit for a single commit (not recommended)
git commit --no-verify

Pre-commit runs automatically on git commit and includes:

• Code formatting with ruff
• Linting with ruff
• Trailing whitespace removal
• File cleanup (EOF, YAML/JSON/TOML validation)
• Large file detection
• Merge conflict detection

License

MIT