An MCP (Model Context Protocol) server that allows language models to fetch protein information from the UniProt database. This server can be integrated with Claude Desktop and other MCP-compatible AI applications.

• Search UniProt: Search for proteins by name, gene, or other criteria
• Get Protein Details: Retrieve detailed information about a protein
• Get Protein Sequences: Fetch amino acid sequences
• Get Protein Functions: Retrieve functional annotations including GO terms
• Get Protein Structures: Find related PDB structures

1. Clone this repository:

   git clone https://github.com/yourusername/uniprot-mcp-server.git
   cd uniprot-mcp-server

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

1. Make the server script executable:

   chmod +x server.py

2. Configure Claude Desktop to use this MCP server:

   For macOS/Linux:

   code ~/Library/Application\ Support/Claude/claude_desktop_config.json

   For Windows:

   code $env:AppData\Claude\claude_desktop_config.json

3. Add the following configuration (update the path to match your actual file location):

   {
     "mcpServers": {
       "uniprot": {
         "command": "python",
         "args": [
           "/ABSOLUTE/PATH/TO/uniprot-mcp-server/server.py"
         ]
       }
     }
   }

4. Restart Claude Desktop

Once set up with Claude Desktop, you can ask questions like:

• "Find proteins related to Alzheimer's disease"
• "Get the sequence for protein P05067 (Amyloid-beta precursor protein)"
• "What are the functions of the TP53 protein?"
• "Are there any known 3D structures for BRCA1 protein?"

To run the server directly for testing:

python server.py

This project includes two testing utilities to verify functionality:

Verifies that the MCP server starts correctly without errors:

python server_health_check.py

Tests connectivity with the UniProt API and verifies that all API calls work as expected:

python test_server.py

Run these tests before using the server to ensure everything is properly configured.

MIT

Contributions are welcome! Please feel free to submit a Pull Request.

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