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