UniProt MCP Server

Fetch protein information from the UniProt database.

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

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

Create a virtual environment (optional but recommended):

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

Configure Claude Desktop to use this MCP server:

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

code $env:AppData\Claude\claude_desktop_config.json

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" ] } } }

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?"

This project includes two testing utilities to verify functionality:

Verifies that the MCP server starts correctly without errors:

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

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

Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (git checkout -b feature/amazing-feature)
- Commit your changes (git commit -m 'Add some amazing feature')
- Push to the branch (git push origin feature/amazing-feature)
- Open a Pull Request

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