datagouv-mcp-server

name	datagouv-mcp-server
description	Use the data.gouv.fr MCP server to search, explore, and analyze French Open Data datasets through AI chatbots
triggers	["How do I connect to the data.gouv.fr MCP server?","Set up the datagouv MCP server in Claude Desktop","Search French open data using MCP","Configure datagouv MCP for ChatGPT","What datasets are available on data.gouv.fr?","Run the datagouv MCP server locally","Connect datagouv MCP to Cursor","Install the French open data MCP server"]

datagouv-mcp-server

Skill by ara.so — MCP Skills collection.

Overview

The data.gouv.fr MCP Server is a Model Context Protocol (MCP) server that enables AI chatbots (Claude, ChatGPT, Gemini, etc.) to search, explore, and analyze datasets from data.gouv.fr, the French national Open Data platform, directly through conversation.

Instead of manually browsing the website, users can ask natural language questions like:

• "Quels jeux de données sont disponibles sur les prix de l'immobilier?"
• "Montre-moi les dernières données de population pour Paris"

Key Features:

• Read-only access to French Open Data (no API key required)
• Search datasets by keywords, topics, and filters
• Explore dataset metadata, resources, and organizations
• Works with all major MCP-compatible chatbots
• Public hosted instance at https://mcp.data.gouv.fr/mcp
• Self-hostable with Docker or Python

Installation & Configuration

Using the Public Hosted Instance (Recommended)

The easiest way to use this MCP server is to connect to the public instance at https://mcp.data.gouv.fr/mcp. No installation required.

Client-Specific Configuration

Claude Desktop

Add to ~/.config/Claude/claude_desktop_config.json (Linux), ~/Library/Application Support/Claude/claude_desktop_config.json (macOS), or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "datagouv": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://mcp.data.gouv.fr/mcp"
      ]
    }
  }
}

Windows-specific fix: If the server doesn't connect, add this at the root level:

{
  "isUsingBuiltInNodeForMcp": false,
  "mcpServers": {
    "datagouv": {
      "command": "npx",
      "args": ["mcp-remote", "https://mcp.data.gouv.fr/mcp"]
    }
  }
}

ChatGPT (Paid Plans Only)

1. Go to Settings → Apps and connectors
2. Enable Developer mode in Advanced settings
3. Go to Connectors → Browse connectors → Add a new connector
4. Set URL to https://mcp.data.gouv.fr/mcp and save

Cursor

In Cursor Settings, search for "MCP" and add:

{
  "mcpServers": {
    "datagouv": {
      "url": "https://mcp.data.gouv.fr/mcp",
      "transport": "http"
    }
  }
}

VS Code

Run MCP: Open User Configuration from Command Palette, then add:

{
  "servers": {
    "datagouv": {
      "url": "https://mcp.data.gouv.fr/mcp",
      "type": "http"
    }
  }
}

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "datagouv": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://mcp.data.gouv.fr/mcp"]
    }
  }
}

Le Chat (Mistral)

1. Go to Intelligence → Connectors
2. Click Add connector → Custom MCP Connector
3. Name it (e.g., "DataGouv")
4. Set URL to https://mcp.data.gouv.fr/mcp
5. Leave authentication disabled and click Create

HuggingChat

1. Click + icon → MCP Servers → Manage MCP Servers
2. Click + Add Server
3. Enter Server Name (e.g., "Data Gouv")
4. Set Server URL to https://mcp.data.gouv.fr/mcp
5. Click Add Server, then Health Check to verify

Running Locally

Prerequisites

• Docker & Docker Compose (recommended)
• OR Python with uv installed

With Docker (Recommended)

# Clone the repository
git clone [e-mail]:datagouv/datagouv-mcp.git
cd datagouv-mcp

# Run with default settings (port 8000, prod environment)
docker compose up -d

# Run with custom settings
MCP_PORT=8007 DATAGOUV_API_ENV=demo LOG_LEVEL=DEBUG docker compose up -d

# Stop
docker compose down

With Python/uv

# Clone the repository
git clone [e-mail]:datagouv/datagouv-mcp.git
cd datagouv-mcp

# Install dependencies
uv sync

# Create environment file
cp .env.example .env

# Edit .env as needed (optional)
# MCP_HOST=127.0.0.1
# MCP_PORT=8007
# DATAGOUV_API_ENV=prod
# LOG_LEVEL=INFO

# Load environment variables
set -a && source .env && set +a

# Start the server
uv run main.py

Environment Variables

Variable	Default	Description
MCP_HOST	0.0.0.0	Host to bind to (use 127.0.0.1 for local dev)
MCP_PORT	8000	Port for the MCP HTTP server
MCP_ENV	local	Environment name (for Sentry): local, prod, preprod, demo
DATAGOUV_API_ENV	prod	data.gouv.fr environment: prod or demo
LOG_LEVEL	INFO	Python logging level: DEBUG, INFO, WARNING, ERROR, CRITICAL
SENTRY_DSN	(unset)	Sentry DSN for error monitoring (optional)
SENTRY_SAMPLE_RATE	1.0	Sentry trace sampling rate (0.0-1.0)

Using the MCP Server

Once configured, you can interact with the MCP server through your AI chatbot by asking questions in natural language.

Example Queries

Search datasets:

"Find datasets about real estate prices in France"
"Quels jeux de données existent sur la pollution de l'air?"
"Show me population data for Paris"

Explore organizations:

"What datasets does INSEE publish?"
"List all datasets from the Ministry of Health"

Dataset details:

"Give me information about dataset ID abc123"
"What resources are available in this dataset?"

Available MCP Tools

The server exposes read-only tools for searching and exploring data.gouv.fr:

search_datasets

Search for datasets by keywords, topics, organizations, etc.

Parameters:

• q (string, optional): Search query
• page_size (int, optional): Results per page (default: 20)
• page (int, optional): Page number (default: 1)
• Additional filters: organization, tag, badge, featured, temporal_coverage, granularity, schema, license

get_dataset

Retrieve detailed information about a specific dataset.

Parameters:

• dataset_id (string, required): The dataset ID or slug

list_resources

List all resources (files, APIs) within a dataset.

Parameters:

• dataset_id (string, required): The dataset ID or slug

get_resource

Get detailed information about a specific resource.

Parameters:

• resource_id (string, required): The resource ID

Code Examples

Python Client Example

import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def search_datasets():
    server_params = StdioServerParameters(
        command="npx",
        args=["mcp-remote", "https://mcp.data.gouv.fr/mcp"]
    )
    
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            # Initialize the connection
            await session.initialize()
            
            # Search for datasets
            result = await session.call_tool(
                "search_datasets",
                arguments={"q": "population", "page_size": 5}
            )
            
            print(result)

asyncio.run(search_datasets())

Connecting a Custom Local Server

If you're running the server locally on port 8007:

{
  "mcpServers": {
    "datagouv-local": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "http://127.0.0.1:8007"
      ]
    }
  }
}

Docker Compose Custom Configuration

# docker-compose.yml
services:
  mcp-server:
    build: .
    ports:
      - "8007:8007"
    environment:
      - MCP_HOST=0.0.0.0
      - MCP_PORT=8007
      - MCP_ENV=prod
      - DATAGOUV_API_ENV=prod
      - LOG_LEVEL=INFO
      - SENTRY_DSN=${SENTRY_DSN}
    restart: unless-stopped

Common Patterns

Searching with Filters

When helping users search datasets, combine text queries with filters:

# Search for recent environmental datasets from a specific org
result = await session.call_tool(
    "search_datasets",
    arguments={
        "q": "environment climate",
        "organization": "ademe",
        "badge": "climate-change",
        "page_size": 10
    }
)

Progressive Exploration

1. Start with broad search
2. Get dataset details with get_dataset
3. List resources with list_resources
4. Get specific resource details with get_resource

Handling Pagination

# Get first page
page1 = await session.call_tool(
    "search_datasets",
    arguments={"q": "transport", "page": 1, "page_size": 20}
)

# Get next page
page2 = await session.call_tool(
    "search_datasets",
    arguments={"q": "transport", "page": 2, "page_size": 20}
)

Troubleshooting

Server Not Connecting in Claude Desktop (Windows)

Symptom: Server appears in list but never connects, no tools visible

Solution: Add "isUsingBuiltInNodeForMcp": false to the root of claude_desktop_config.json:

{
  "isUsingBuiltInNodeForMcp": false,
  "mcpServers": { ... }
}

See issue #69

Connection Timeout

Symptom: Server fails to respond or times out

Solutions:

1. Verify the public instance is up: curl https://mcp.data.gouv.fr/mcp
2. Check your network/firewall settings
3. Try running a local instance instead

Local Server Won't Start

Symptom: Error when running uv run main.py

Solutions:

1. Ensure uv is installed: pip install uv
2. Verify Python version compatibility (check pyproject.toml)
3. Check .env file exists and is loaded
4. Review logs with LOG_LEVEL=DEBUG

No Results Returned

Symptom: Search returns empty results

Solutions:

1. Verify DATAGOUV_API_ENV is set correctly (prod vs demo)
2. Try broader search terms
3. Check if specific filters are too restrictive
4. Test the same query on https://www.data.gouv.fr directly

CORS Issues (Browser-Based Clients)

Symptom: CORS errors in browser console

Solution: The public instance should handle CORS. If self-hosting, ensure your server configuration allows CORS from your client origin.

Development & Testing

Run Tests

# Install dev dependencies
uv sync --dev

# Run tests
uv run pytest

# Run with coverage
uv run pytest --cov=src

Enable Debug Logging

LOG_LEVEL=DEBUG uv run main.py

Test Against Demo Environment

DATAGOUV_API_ENV=demo uv run main.py

This connects to https://demo.data.gouv.fr instead of production.

Resources

• Project Repository: https://github.com/datagouv/datagouv-mcp
• Public Instance: https://mcp.data.gouv.fr/mcp
• data.gouv.fr Platform: https://www.data.gouv.fr
• MCP Specification: https://modelcontextprotocol.io
• Feedback Form: https://tally.so/r/KYMboX

License

MIT License - See the repository for full details.