Climate Data Store (CDS) MCP Server

The Climate Data Store (CDS) MCP Server bridges the gap between conversational AI assistants and the rich repository of climate data maintained by Copernicus. By exposing a set of intuitive tools that mirror the underlying API, it allows developers to embed climate‑science workflows directly into AI interactions. Instead of manually querying the CDS portal or writing bespoke scripts, an assistant can now ask for specific datasets, submit download jobs, and retrieve results—all through a single, well‑defined protocol.

At its core, the server offers five principal tools:

• – lists existing job identifiers, optionally filtered by status.
• – pulls the finished output for a given job ID.
• – enumerates every dataset collection available in the catalogue.
• – fetches detailed metadata for a specific collection.
• – initiates a new download request by specifying the desired collection and parameters.

These operations map directly onto the user‑facing actions a scientist or data engineer would perform on the CDS website, but they are now accessible programmatically through MCP. The server reads configuration from a file, simplifying credential management and enabling secure deployment in CI/CD pipelines or local development environments.

For developers building AI‑augmented data science tools, this MCP server is a game‑changer. It lets an assistant act as a first‑class data curator: users can ask for “the latest temperature anomaly dataset for the Arctic” and receive a ready‑to‑use file, or they can request “all available datasets that include precipitation.” Because the server communicates over MCP, any client—Claude Desktop, the MCP Inspector, or custom tooling—can tap into these capabilities without needing to understand the intricacies of the CDS API.

Real‑world scenarios include automated climate monitoring dashboards, research assistants that pull up historical data during literature reviews, and educational platforms where students can experiment with real meteorological datasets through conversational prompts. The server’s integration points are minimal: once installed, it can be referenced in a client’s MCP configuration, and the tools become part of the assistant’s repertoire. This seamless workflow eliminates manual data handling steps, reduces errors, and accelerates scientific discovery.

Unique advantages of the CDS MCP Server stem from its tight coupling to Copernicus’ authoritative data, combined with a lightweight Python implementation that leverages for dependency management. It supports environment variable injection, making it suitable for both local experimentation and production deployments behind corporate firewalls. By exposing a clean, declarative interface over MCP, it empowers developers to harness climate data without wrestling with authentication tokens or REST endpoint details.