UniProt MCP Server: Enhancing AI Application Integration

Overview: What is UniProt MCP Server?

The UniProt MCP Server is an essential tool designed to facilitate seamless integration of AI applications, particularly those running on platforms like Claude Desktop, Continue, and Cursor. By adhering to the Model Context Protocol (MCP), this server enables AI applications to access detailed protein data from UniProt, a premier database for information about proteins.

🔧 Core Features & MCP Capabilities

The UniProt MCP Server is equipped with several key features that make it highly capable in its role as an MCP server:

1. Protein Information Retrieval: Users can fetch comprehensive protein details including names, functions, sequences, lengths, and organisms by providing a UniProt accession number.
2. Batch Processing: The ability to handle multiple protein queries efficiently through batch processing improves the speed and efficiency of data retrieval processes.
3. Caching Mechanism: An intelligent caching system with a 24-hour Time-to-Live (TTL) is implemented to enhance performance by reducing redundant requests, thereby optimizing resource usage.
4. Error Handling & Logging: Robust error handling mechanisms ensure that any issues during interaction with UniProt are logged and the application can provide meaningful feedback.

Detailed MCP Protocol Implementation

1. MCP Client Compatibility Matrix
   • Claude Desktop: Supports full integration of the UniProt data, allowing for seamless retrieval of protein details.
   • Continue: Compatible, but requires manual configuration due to limited support for certain prompts and resources.
   • Cursor: Limited compatibility focusing solely on tool usage without AI application interaction.

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

2. MCP Protocol Flow Diagram

   graph TD;
       A[AI Application] -->|MCP Client| B[MCP Server]
       B --> C[MCP API Endpoint]
       C --> D[UniProt Database]
       style A fill:#e1f5fe; stroke-width:2px
       style C fill:#f3e5f5; stroke-width:2px
       style D fill:#e8f5e8; stroke-width:2px
   

Real-World AI Workflow Use Cases

Imagine an AI project where biologists need to rapidly query and analyze protein sequences:

1. Use Case 1: Prokaryotic Protein Analysis

   • A biologist queries the UniProt MCP Server with a specific access code for a prokaryotic protein. The server retrieves the sequence, function description, and organism information almost instantly.

2. Use Case 2: Pathway Exploration & Annotation

   • In a genetic research workflow, scientists utilize the UniProt MCP to batch fetch multiple proteins involved in specific pathways. The tool then processes this data, providing annotations that can be integrated into larger analyses, such as drug discovery or gene editing projects.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the UniProt MCP Server is designed around efficiency and reliability:

1. Built Using MCP Python SDK: This ensures compatibility with various MCP clients without requiring modifications.
2. httpx for Async HTTP Requests: Utilizes asynchronous requests to handle multiple queries efficiently, minimising wait times during data retrieval.
3. Caching Mechanism: Implemented using an OrderedDict-based cache that automatically manages storage and evicts expired entries to keep the cache fresh.

🚀 Getting Started with Installation

Setting up the UniProt MCP Server is straightforward:

1. Prerequisites
   • Ensure Python 3.10 or higher is installed on your system.
2. Clone Repository

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

3. Install Dependencies
   • For uv (recommended)

     uv pip install -r requirements.txt
     

   • Or using standard pip

     pip install -r requirements.txt
     

💡 Key Use Cases in AI Workflows

1. Protein Annotation & Analysis
   • Integrating the UniProt MCP Server into an AI application can enable quick and accurate annotation of proteins, enhancing the bioinformatics capabilities.
2. Drug Discovery Pipeline
   • During a drug discovery process, the server can be used to query multiple protein targets efficiently, reducing the time required for initial screening.

🔌 Integration with MCP Clients

To integrate the UniProt MCP Server into AI applications like Claude Desktop:

1. Update your Claude Desktop Config File:

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

2. Verify the configuration by running a sample query.

📊 Performance & Compatibility Matrix

The UniProt MCP Server is optimized for various configurations:

• Response Time: Typically under 500ms, depending on network latency.
• Concurrency Limits: Supports up to 100 concurrent queries with dynamic adjustments according to server load.

🛠️ Advanced Configuration & Security

1. Development Environment Setup
   • Clone the repository and create a virtual environment:

     python -m venv .venv
     source .venv/bin/activate  # For macOS/Linux
     .venv\Scripts\activate  # For Windows
     

   • Install development dependencies via pip install -e ".[dev]".
2. Running Tests

   pytest
   

❓ Frequently Asked Questions (FAQ)

Why does the server sometimes fail to retrieve information?

• Invalid Accession Numbers: Ensure the access codes provided are valid and accessible in UniProt.
• Network Issues: Check your internet connection and try again.

Can I cache data for longer than 24 hours?

• No, currently the server is configured with a 24-hour TTL. This setting ensures fresh data while optimizing performance.

How do I handle rate limiting during requests to the UniProt API?

• The server has built-in mechanisms to automatically retry and back off in case of temporary unavailability or rate limits from UniProt.

👨‍💻 Development & Contribution Guidelines

Contributions are welcome! Follow these steps:

1. Fork the Repository
2. Create a Feature Branch

   git checkout -b feature/feature-name
   

3. Commit Your Changes

   git commit -m 'Add some amazing feature'
   

4. Push to Your Branch

   git push origin feature/feature-name
   

5. Open a Pull Request

We appreciate your efforts in making this project better!

🌐 MCP Ecosystem & Resources

Explore more about the Model Context Protocol and its various adapters: ModelContextProtocol.org

Key Tools & Resources:

• Black for Code Formatting
• isort for Import Sorting
• Flake8 for Linting
• Mypy for Type Checking
• Bandit for Security Checks
• Safety for Dependency Vulnerability Checks

Conclusion

The UniProt MCP Server is a powerful tool that accelerates the integration of AI applications with vital biological data. By adhering to the Model Context Protocol, it ensures seamless and reliable communication between various platforms and backend services.

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Key Takeaways:

• Integration Flexibility: Supports multiple MCP clients including Claude Desktop.
• Performance Optimization: Caching and asynchronous processing enhance performance.
• Detailed Documentation: Comprehensive setup and usage guides ensure ease of deployment.

Feel free to explore, contribute, or integrate the UniProt MCP Server into your AI workflows today!