UniProt MCP Server: Comprehensive API Integration for Intelligent Data Access

Overview: What is UniProt MCP Server?

The UniProt MCP Server is a specialized Model Context Protocol (MCP) server that integrates seamlessly with AI applications, specifically Claude Desktop and other compatible clients. It functions as a bridge between AI applications and the vast repository of protein information provided by UniProt. This makes it an invaluable tool for developers building intelligent applications that require detailed biological data.

🔧 Core Features & MCP Capabilities

The UniProt MCP Server offers several features that enhance its utility in various AI workflows, making it a robust MCP client:

1. Access to Protein Information: Users can retrieve comprehensive information about proteins by providing their UniProt accession numbers. This includes details such as protein names, function descriptions, full sequences, and organism information.
2. Batch Retrieval Mode: The server supports the batch retrieval of multiple protein entries simultaneously, which significantly improves efficiency for processing large datasets.
3. Caching Mechanism: Implementing caching with a 24-hour Time-to-Live (TTL) increases performance by reducing redundant database access and improving response times.
4. Error Handling & Logging: The server includes robust error handling mechanisms to deal with various issues, such as malformed responses or API connection errors. Detailed logging ensures troubleshooting can be conducted effectively.

⚙️ MCP Architecture & Protocol Implementation

MCP Protocol Implementation Details

The UniProt MCP Server is built using the Model Context Protocol (MCP), a protocol designed for uniform interaction between AI applications and external data sources. It leverages the MCP Python SDK to ensure seamless integration with other MCP clients, adhering strictly to the protocol specifications provided by Anthropic.

AI Application Compatibility

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	❌	Full Support (Tools Only)
Cursor	❌	✅	❌	Tools Only

Real-world AI Workflow Use Cases

1. Protein Function Analysis: An AI biochemist might use the UniProt MCP Server to fetch and analyze protein sequences and their functions, which could inform drug development or disease research.
2. Genomics Research: Geneticists can integrate this server into genomics pipelines to annotate proteins based on their UniProt information, enhancing understanding of genetic variations.

🚀 Getting Started with Installation

To set up the UniProt MCP Server, follow these steps:

1. Ensure you have Python 3.10 or higher installed.
2. Clone the repository:

   git clone https://github.com/TakumiY235/uniprot-mcp-server.git
   cd uniprot-mcp-server
   

3. Install dependencies using uv (recommended) or pip:

   uv pip install -r requirements.txt  # For uv command support
   pip install -r requirements.txt      # Using standard pip
   

💡 Key Use Cases in AI Workflows

For developers focusing on bioinformatics and genomics, the UniProt MCP Server plays a crucial role by providing quick access to critical protein data. Its integration with tools like Claude Desktop allows for real-time interaction and analysis, enabling researchers to work more efficiently.

🔌 Integration with MCP Clients

To ensure compatibility with other MCP clients, add the following configuration snippet to your Claude Desktop config file:

{
  "mcpServers": {
    "uniprot": {
      "command": "uv",
      "args": ["--directory", "path/to/uniprot-mcp-server", "run", "uniprot-mcp-server"]
    }
  }
}

This setup ensures that Claude Desktop can natively communicate with the UniProt MCP Server when performing relevant commands.

📊 Performance & Compatibility Matrix

Client	AI Application Features
Claude Desktop	- Full support for all API calls <br> - Real-time integration <br> - High availability and reliability

This matrix highlights the strength of integration, ensuring optimal performance and reliability when using Claude Desktop with the UniProt MCP Server.

🛠️ Advanced Configuration & Security

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

Execute the necessary checks to ensure code integrity and security:

black .
isort .
flake8 .
mypy .
bandit -r src/
safety check

❓ Frequently Asked Questions (FAQ)

1. How does the UniProt MCP Server handle caching? The server uses an OrderedDict-based cache with a 24-hour TTL, which helps in managing memory-efficiently and keeping up with frequently accessed data.

2. What error scenarios does the server support? The server is equipped to handle various errors such as invalid accessions, network issues, rate limits, malformed responses, and cache management through detailed logging mechanisms.

3. Can I use different MCP clients with this server? Yes, while Claude Desktop has full support, other tools like Continue are also compatible but might have limitations in some functionalities due to their architecture differences.

4. How can I contribute to the project? Contributions are welcome! Fork the repository, make your changes, and submit a Pull Request adhering to our existing coding style guidelines.

5. Is there any specific tool or library for managing MCP connections in this setup? The server utilizes tools like Black for code formatting, isort for import sorting, flake8 for linting, mypy for type checking, bandit for security checks, and safety for detecting potential vulnerabilities.

👨‍💻 Development & Contribution Guidelines

1. Fork the repository.
2. Create a feature branch: git checkout -b feature/your-feature-name
3. Commit your changes: git commit -m 'Implemented new feature x'
4. Push to the branch: git push origin feature/your-feature-name
5. Open a Pull Request.

Ensure tests are updated and adhere to our coding style guidelines for smooth integration into the project.

🌐 MCP Ecosystem & Resources

The UniProt MCP Server is part of a broader ecosystem supporting developers engaged in building intelligent applications that require data from various sources. Explore additional resources, such as Anthropic’s Model Context Protocol documentation, to enhance your understanding and implementation strategies.

MCP Protocol Flow Diagram

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

MCP Client Compatibility Matrix

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	❌	Full Support (Tools Only)

By following this comprehensive documentation, developers will be well-equipped to utilize the UniProt MCP Server for efficient and reliable access to protein information within their AI applications.