Data Gouv MCP Server

The Data Gouv MCP Server bridges Claude‑style AI assistants with the public French data portal, data.gouv.fr. By exposing the Recherche Entreprises API through a Model Context Protocol (MCP) endpoint, developers can query real‑time company information directly from within an AI workflow. This eliminates the need for manual API calls, authentication handling, and data parsing—tasks that are traditionally cumbersome when integrating external services into conversational agents.

What Problem Does It Solve?

Many organizations rely on up‑to‑date corporate data for compliance, risk assessment, or market research. Fetching this information manually from data.gouv.fr requires HTTP requests, JSON handling, and error management. The MCP server abstracts these details behind a single, well‑defined tool (). AI assistants can now request company data with natural language prompts, and the server translates those into structured API calls. This streamlines development, reduces boilerplate code, and ensures that the AI can reliably access authoritative French corporate records.

Core Functionality and Value

At its heart, the server implements a single tool: . When invoked with a search query—such as a company name, CEO, or address—the tool contacts the Recherche Entreprises API and returns key attributes: legal name, registered address, list of directors, sector classification, and more. Because the server communicates over HTTP+SSE (Server‑Sent Events), responses are streamed in real time, allowing the AI to begin processing data before the entire payload is received. This results in faster interactions and a smoother conversational experience.

Developers benefit from:

• Zero‑configuration access: No API keys or OAuth flows are required; the MCP server handles all network details.
• Consistent data schema: The tool’s output follows a predictable structure, simplifying downstream processing.
• Scalable integration: The server can be deployed behind a reverse proxy, load‑balanced, or containerized to meet production demands.

Key Features Explained

• HTTP+SSE Transport: Leveraging the MCP specification, the server streams results directly to the client, improving latency and user experience.
• Tool‑centric Design: Only one focused tool () keeps the interface simple and prevents accidental misuse of the underlying API.
• TypeScript Implementation: Built with modern Node.js (Node 22) and pnpm, the codebase benefits from type safety and rapid iteration.

Real‑World Use Cases

• Regulatory Compliance: An AI assistant can verify the legal status of a potential supplier before initiating procurement.
• Financial Analysis: Analysts use the tool to pull executive lists and sector codes for portfolio screening.
• Market Intelligence: Start‑ups query competitor company details to inform strategic planning.
• Customer Support Automation: Bots answer user inquiries about a company’s address or directors without leaving the chat interface.

Integration with AI Workflows

To incorporate the server, a developer simply adds its SSE endpoint to their MCP client configuration. Once registered, any prompt that includes “search for company X” will trigger the tool. The AI can then embed the returned data into a response, use it to populate tables, or pass it to other downstream tools. Because the server adheres strictly to MCP conventions, any client that supports the protocol—Claude, Gemini, or custom assistants—can consume it without additional adapters.

Standout Advantages

• Simplicity: A single, well‑documented tool lowers the learning curve for developers new to MCP.
• Real‑time Streaming: SSE support means users see partial results quickly, enhancing interactivity.
• Open Source and Extensible: Built on top of the MCP specification, the server can be forked and extended to expose additional data.gouv.fr endpoints or custom logic.

In summary, the Data Gouv MCP Server turns a complex public API into an effortless, AI‑ready resource. By packaging company search functionality behind a concise MCP tool, it empowers developers to deliver richer, data‑driven conversations with minimal overhead.