Biomart MCP Server: Seamless Integration of Biological Databases into AI Workflows

Overview: What is Biomart MCP Server?

Biomart MCP Server is an advanced MCP (Model Context Protocol) server tailored to interface with biological databases, specifically leveraging the powerful capabilities of pybiomart. This server acts as a bridge between machine learning and artificial intelligence applications and the rich, structured data available through Biomart. By standardizing how these applications provide context to LLMs via MCP, Biomart MCP Server ensures interoperability with various AI tools, enhancing their functionality in biological research and analysis.

🔧 Core Features & MCP Capabilities

Biomart MCP Server offers a robust suite of features designed for seamless integration into artificial intelligence workflows. These include:

Mart and Dataset Discovery

• Discovery Tools: Biomart MCP Server provides extensive capabilities to list available marts and datasets, allowing users to explore the structure and contents of the database.
• Dynamic Contextualization: This feature enables models like Claude Desktop, Continue, and Cursor to dynamically adapt to different biological contexts.

Attribute and Filter Exploration

• Attribute View: Users can view common or all available attributes for specific datasets, enhancing data understanding.
• Filter Navigation: Explore filters that refine the retrieved data, providing granular control over query outputs.

Data Retrieval

• Customized Queries: Query Biomart databases with precision, using a combination of attributes and filters to retrieve specific biological data. This powerful feature is essential for research and analysis in bioinformatics.

ID Translation

• Identifier Conversion: Convert between different biological identifiers (e.g., gene symbols to Ensembl IDs), ensuring consistent and accurate data handling.

These capabilities are implemented through the use of MCP, a universal adapter for integrating AI applications with external data sources. By leveraging MCP, Biomart MCP Server ensures compatibility across various platforms, making it a versatile tool in the field of biological research and analysis.

⚙️ MCP Architecture & Protocol Implementation

At its core, Biomart MCP Server implements the Model Context Protocol (MCP) to standardize how AI models can interact with external data sources. The server is built using the MCP Python SDK, and is fully compatible with MCP clients such as Claude Desktop and Continue.

Mermaid Diagram: MCP Protocol Flow

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

Mermaid Diagram: Data Architecture

graph LR
    A[Client] -- HTTP --> B[MCP Server] -- Data --> C[Biological Databases] 
    B -- Configuration --> D[Configuration Storage]
    style A fill:#d5ffe1
    style B fill:#f3e5f5
    style C fill:#f5e8ea

The architecture diagram above illustrates the flow of data interaction between an AI application, MCP Server, and biological databases. The server acts as a central hub for processing user queries and retrieving results from the biomart database.

🚀 Getting Started with Installation

Getting started with Biomart MCP Server is straightforward:

Clone the Repository

git clone https://github.com/jzinno/biomart-mcp.git
cd biomart-mcp

Installation for Cursor and Claude Desktop:

uv run --with mcp[cli] mcp install --with pybiomart biomart-mcp.py

• Requirements: Ensure uv is installed. You can install it via npm with the command: npm i -g uv.

Configuration for Cursor:

Via Cursor's agent mode, MCP servers like Biomart can be added to either the global configuration or a specific project.

Example .cursor/mcp.json configuration file:

{
    "mcpServers": {
        "Biomart": {
            "command": "uv",
            "args": [
                "run",
                "--with",
                "mcp[cli]",
                "--with",
                "pybiomart",
                "mcp",
                "run",
                "/your/path/to/biomart-mcp.py"
            ]
        }
    }
}

💡 Key Use Cases in AI Workflows

Use Case 1: Gene Expression Analysis Using Biomart

• Scenario: A researcher aims to analyze gene expression levels across various species and chromosomes.
• Procedure:
  • Discovery: Discover available marts and datasets relevant to the analysis.
  • Query Construction: Build a query using specific attributes such as gene symbols, species, and chromosome information.
  • Result Retrieval: Retrieve and process data for detailed analysis.

Use Case 2: Protein Interactions and Gene Annotations

• Scenario: Integrating protein interaction data with gene annotations to identify regulatory mechanisms.
• Procedure:
  • Attribute Exploration: Explore available attributes and filters to narrow down the dataset.
  • Integration: Combine data from Biomart with external databases like UniProtKB for comprehensive analysis.

🔌 Integration with MCP Clients

Biomart MCP Server is designed to integrate seamlessly with various MCP clients. Here’s a compatibility matrix highlighting support levels:

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

*Tool support on the way for future versions!

📊 Performance & Compatibility Matrix

To ensure optimal performance and compatibility, Biomart MCP Server is equipped with several features:

Caching Mechanisms

• @lru_cache: Utilized to cache results of functions making external API calls. This helps in reducing overloads and improving response times significantly.

Context Window Management

• Token Efficiency: Optimal use of tokens by returning data in CSV format, ensuring no unnecessary whitespace is included.
• Filter Optimization: Careful construction of queries with filters to avoid context window blowups.

🛠️ Advanced Configuration & Security

For advanced users and security considerations:

Configuration Code Sample

{
  "mcpServers": {
    "Biomart": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "--with",
        "pybiomart",
        "mcp",
        "run",
        "/path/to/biomart-mcp.py"
      ]
    }
  },
  "security": {
    "authentication": "api_key",
    "allowed_clients": ["12345-abcde"]
  }
}

This configuration ensures that only authorized clients can interact with the Biomart MCP Server, enhancing security.

❓ Frequently Asked Questions (FAQ)

Q: Can I use Biomart MCP Server with Continue?

A: Yes, Biomart MCP Server is fully compatible with Continue. You can add it to your Continue project using the provided installation commands.

Q: How do I ensure my queries stay within the context window of models like Claude Desktop and Continue?

A: By carefully constructing your queries using efficient filter settings and minimizing unnecessary data retrieval.

Q: Is there a way to integrate more tools beyond pybiomart in Biomart MCP Server?

A: Yes, we are considering integrating additional tools such as web scraping through bs4. This would allow for more diverse data sources to be included in the server's capabilities.

Q: What security measures does Biomart MCP Server offer?

A: The server supports authentication mechanisms and restricts access to only certain clients via API keys. You can customize this in your configuration settings.

Q: Can I run Biomart MCP Server locally for offline use?

A: Yes, you can run the server locally using mcp dev command which allows development mode execution without internet connectivity.

Q: How do I handle large datasets retrieved from Biomart databases?

A: Large datasets are managed through incremental loading and efficient data formatting to avoid reaching model context window limits.

👨‍💻 Development & Contribution Guidelines

Contributions to Biomart MCP Server are welcome! To get started:

1. Clone the Repository: git clone https://github.com/jzinno/biomart-mcp.git
2. Set Up Environment:
   • uv venv
   • source .venv/bin/activate (for macOS/Linux)
   • .venv\Scripts\activate (for Windows)
3. Install Dependencies:
   • uv sync # or uv add mcp[cli] pybiomart
4. Run the Development Server:

   mcp dev biomart-mcp.py
   

🌐 MCP Ecosystem & Resources

Explore more about MCP and its ecosystem on the official Model Context Protocol website. Also, check out the MCP Python SDK documentation for additional resources.

By leveraging Biomart MCP Server, developers can enhance their AI workflows with precise data integration from biological databases. This server plays a crucial role in making complex biological data accessible to a wide range of AI applications.