Climate Data Store (CDS) MCP Server: Connecting AI Applications to Environmental Data

Overview: What is CDS MCP Server?

The CDS MCP Server provides a standardized interface for machine learning models and AI applications to interact with and retrieve data from the Climate Data Store (CDS). By using Model Context Protocol (MCP), this server enables seamless integration with diverse AI tools, enhancing their capabilities in handling complex environmental datasets. This document outlines how developers can install, configure, and utilize the CDS MCP Server.

🔧 Core Features & MCP Capabilities

The core features of the CDS MCP Server include:

• Job Management:

  • get_jobs: Fetches a list of available jobs with optional status filtering.
  • download_job_result: Downloads data results using a job ID.
  • get_all_collections: Lists all collections in the CDS catalogue.
  • get_collection_by_id: Retrieves detailed information about a specific collection.

• Environment Variable Support: Environment variables are configured using a .env file, ensuring secure and efficient operation of the server. Developers can specify API keys, URLs, and other necessary metadata through environment variables to facilitate seamless connectivity with CDS.

⚙️ MCP Architecture & Protocol Implementation

The architecture leverages Model Context Protocol (MCP) for universal client-server communication. This protocol ensures compatibility across various AI applications and environments by providing a standardized framework. The underlying API is datapi, which supports robust retrieval of data from the CDS catalogue. The implementation involves setting up an MCP server that hosts methods to interact with these APIs, ensuring secure and efficient access to climate data.

Mermaid Diagram: MCP Protocol Flow

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

Mermaid Diagram: Data Architecture

graph TD
    A[CDS Catalogue] -->|Query Requests| B[MCP Server]
    B --> C[Job Collection] | API Calls |
    C --> D[Data Results] | Downloaded Data |
    D --> E[AI Applications] | Analysis & Processing |
    style A fill:#b3e5fc
    style C fill:#f3e5f5
    style D fill:#f0f8ff
    style E fill:#c6efce

🚀 Getting Started with Installation

To set up and run the CDS MCP Server, follow these steps:

1. Clone the Repository:

   git clone [email protected]:albertdow/mcp-datapi.git
   cd mcp-datapi
   

2. Install Dependencies:

   uv add "mcp[cli]" datapi python-dotenv
   

3. Setup CDS API Key: Create a .env file and add the following environment variables:

   DATAPI_URL=<DATAPI_URL>
   DATAPI_KEY=<DATAPI_KEY>
   

4. Test Locally: Run the server using MCP Inspector in development mode:

   mcp dev datapi_server.py
   

5. Integrate with Claude Desktop (Optional): Add a configuration to your claude_desktop_config.json file:

   {
       "mcpServers": {
           "DatapiServer": {
               "command": "/Users/username/.local/bin/uv",
               "args": [
                   "--directory",
                   "mcp-datapi",
                   "run",
                   "datapi_server.py"
               ],
               "env": {
                   "DATAPI_URL": "<DATAPI_URL>",
                   "DATAPI_KEY": "<DATAPI_KEY>"
               }
           }
       }
   }
   

💡 Key Use Cases in AI Workflows

Case Study 1: Environmental Impact Assessment

• Description: An environmental consultancy uses the CDS MCP Server to access historical climate data for predictive modeling.
• Implementation: The server is configured to retrieve specific collection IDs and job results, enabling precise data analysis.

{
    "mcpServers": {
        "DatapiServer": {
            "command": "/Users/username/.local/bin/uv",
            "args": [
                "--directory",
                "mcp-datapi",
                "run",
                "datapi_server.py"
            ],
            "env": {
                "DATAPI_URL": "https://cds.climate.copernicus.eu/api/",
                "DATAPI_KEY": "your-api-key"
            }
        }
    }
}

Case Study 2: Climate Modeling

• Description: A climate modeling study requires frequent data updates and complex job dependencies.
• Implementation: The server is set up to manage multiple concurrent jobs, providing real-time status updates and result downloads.

🔌 Integration with MCP Clients

The CDS MCP Server supports a wide range of clients within the AI ecosystem:

• Claude Desktop - Full Support
• Continue - Full Support
• Cursor - Tools Only

This cross-compatibility ensures broad applicability for diverse AI tools and development workflows.

📊 Performance & Compatibility Matrix

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

🛠️ Advanced Configuration & Security

For advanced users, the following environment variables can be configured:

DATAPI_KEY=your-secure-api-key
MCP_SERVER_PORT=8081

Ensure secure storage and transmission of sensitive data to maintain server integrity.

❓ Frequently Asked Questions (FAQ)

1. Which Python versions are supported?

The CDS MCP Server supports Python 3.13 or higher.

2. How do I set up the .env file correctly?

Create a .env file and add these variables:

DATAPI_URL=https://cds.climate.copernicus.eu/api/
DATAPI_KEY=your-api-key

3. Can I integrate this server with other MCP clients?

Yes, the CDS MCP Server is compatible with various MCP clients including Claude Desktop and Continue.

4. What happens if I don't have an API key?

You need to create a CDS API Key from the official CDS portal to authenticate your requests.

5. How do I troubleshoot server crashes or errors?

Check logs for error messages, typically found in datapi_server.log within the project directory.

👨‍💻 Development & Contribution Guidelines

To contribute to the development of this repository:

1. Fork and Clone: Fork the repository on GitHub and clone it locally.
2. Development Environment: Set up a virtual environment and install dependencies as specified in requirements.txt.
3. Code Contributions:
   • Adhere to PEP8 coding standards.
   • Submit pull requests for bug fixes or new features.

🌐 MCP Ecosystem & Resources

For further details about Model Context Protocol and related resources, visit the following links:

• MCP Protocol Documentation
• CDS API Documentation

---

This comprehensive document provides a detailed guide for integrating the CDS MCP Server into AI workflows, emphasizing its compatibility and utility in various technological contexts.