UniProt MCP Server

UniProt MCP Server

A Model Context Protocol (MCP) server that provides access to UniProt protein information. This server allows AI assistants to fetch protein function and sequence information directly from UniProt.

Features

• Get protein information by UniProt accession number
• Batch retrieval of multiple proteins
• Caching for improved performance (24-hour TTL)
• Error handling and logging
• Information includes:
  • Protein name
  • Function description
  • Full sequence
  • Sequence length
  • Organism

Quick Start

1. Ensure you have Python 3.10 or higher installed
2. Clone this repository:
   git clone https://github.com/TakumiY235/uniprot-mcp-server.git cd uniprot-mcp-server
3. Install dependencies:
   # Using uv (recommended) uv pip install -r requirements.txt # Or using pip pip install -r requirements.txt

Configuration

Add to your Claude Desktop config file:

• Windows: %APPDATA%\Claude\claude_desktop_config.json
• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

Usage Examples

After configuring the server in Claude Desktop, you can ask questions like:

For batch queries:

API Reference

Tools

1. get_protein_info
   • Get information for a single protein
   • Required parameter: accession (UniProt accession number)
   • Example response:
     { "accession": "P12345", "protein_name": "Example protein", "function": ["Description of protein function"], "sequence": "MLTVX...", "length": 123, "organism": "Homo sapiens" }
2. get_batch_protein_info
   • Get information for multiple proteins
   • Required parameter: accessions (array of UniProt accession numbers)
   • Returns an array of protein information objects

Development

Setting up development environment

1. Clone the repository
2. Create a virtual environment:
   python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate
3. Install development dependencies:
   pip install -e ".[dev]"

Running tests

Code style

This project uses:

• Black for code formatting
• isort for import sorting
• flake8 for linting
• mypy for type checking
• bandit for security checks
• safety for dependency vulnerability checks

Run all checks:

Technical Details

• Built using the MCP Python SDK
• Uses httpx for async HTTP requests
• Implements caching with 24-hour TTL using an OrderedDict-based cache
• Handles rate limiting and retries
• Provides detailed error messages

Error Handling

The server handles various error scenarios:

• Invalid accession numbers (404 responses)
• API connection issues (network errors)
• Rate limiting (429 responses)
• Malformed responses (JSON parsing errors)
• Cache management (TTL and size limits)

Contributing

We welcome contributions! Please feel free to submit a Pull Request. Here's how you can contribute:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Please make sure to update tests as appropriate and adhere to the existing coding style.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• UniProt for providing the protein data API
• Anthropic for the Model Context Protocol specification
• Contributors who help improve this project