Official data.gouv.fr Model Context Protocol (MCP) server that allows AI chatbots to search, explore, and analyze datasets from the French national Open Data platform, directly through conversation.

Model Context Protocol (MCP) server that allows 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, you can simply ask questions like "Quels jeux de données sont disponibles sur les prix de l'immobilier ?" or "Montre-moi les dernières données de population pour Paris" and get instant answers.

Use the hosted endpoint https://mcp.data.gouv.fr/mcp (recommended). If you self-host, swap in your own URL.

The MCP server configuration depends on your client. Use the appropriate configuration format for your client:

AnythingLLM | ChatGPT | Claude Code | Claude Desktop | Cursor | Gemini CLI | IBM Bob | Kiro CLI | Kiro IDE | Mistral Vibe | VS Code | Windsurf

1. Locate the anythingllm_mcp_servers.json file in your AnythingLLM storage plugins directory:

   • Mac: ~/Library/Application Support/anythingllm-desktop/storage/plugins/anythingllm_mcp_servers.json
   • Linux: ~/.config/anythingllm-desktop/storage/plugins/anythingllm_mcp_servers.json
   • Windows: C:\Users\<username>\AppData\Roaming\anythingllm-desktop\storage\plugins\anythingllm_mcp_servers.json

2. Add the following configuration:

{
  "mcpServers": {
    "datagouv": {
      "type": "streamable",
      "url": "https://mcp.data.gouv.fr/mcp"
    }
  }
}

For more details, see the AnythingLLM MCP documentation.

Available for paid plans only (Plus, Pro, Team, and Enterprise).

1. Access Settings: Open ChatGPT in your browser, go to Settings, then Apps and connectors.
2. Enable Dev Mode: Open Advanced settings and enable Developer mode.
3. Add Connector: Return to Settings > Connectors > Browse connectors and click Add a new connector.
4. Configure the connector: Set the URL to https://mcp.data.gouv.fr/mcp and save to activate the tools.

Use the claude mcp command to add the MCP server:

claude mcp add --transport http datagouv https://mcp.data.gouv.fr/mcp

Add the following to your Claude Desktop configuration file (typically ~/Library/Application Support/Claude/claude_desktop_config.json on MacOS, or %APPDATA%\Claude\claude_desktop_config.json on Windows):

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

Cursor supports MCP servers through its settings. To configure the server:

1. Open Cursor Settings
2. Search for "MCP" or "Model Context Protocol"
3. Add a new MCP server with the following configuration:

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

Add the following to your ~/.gemini/settings.json file:

{
  "mcpServers": {
    "datagouv": {
      "httpUrl": "https://mcp.data.gouv.fr/mcp"
    }
  }
}

Edit your Vibe config (default ~/.vibe/config.toml) and add the MCP server:

[[mcp_servers]]
name = "datagouv"
transport = "streamable-http"
url = "https://mcp.data.gouv.fr/mcp"

See the full Vibe MCP options in the official docs: MCP server configuration.

Add the following to your Kiro MCP configuration file (.kiro/settings/mcp.json in your workspace, or ~/.kiro/settings/mcp.json for global config):

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

Add the following to ~/.kiro/settings/mcp.json:

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

IBM Bob supports MCP servers through its settings. To configure the server:

1. Click the setting icon in the Bob panel.
2. Select the MCP tab.
3. Click the appropriate button:

• Edit Global MCP: Opens the global mcp_settings.json file
• Edit Project MCP: Opens the project-specific .bob/mcp.json file (Bob creates it if it does not exist)

Both files use JSON format with an mcpServers object containing named server configurations.

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

Add the following to your VS Code settings.json:

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

Add the following to your ~/.codeium/mcp_config.json:

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

Note:

• The hosted endpoint is https://mcp.data.gouv.fr/mcp. If you run the server yourself, replace it with your own URL (see “Run locally” below for the default local endpoint).
• This MCP server only exposes read-only tools for now, so no API key is required.

Before starting, clone this repository and browse into it:

git clone git@github.com:datagouv/datagouv-mcp.git
cd datagouv-mcp

Docker is required for the recommended setup. Install it via Docker Desktop or any compatible Docker Engine before continuing.

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

# With custom environment variables
MCP_PORT=8007 DATAGOUV_ENV=demo docker compose up -d

# Stop
docker compose down

Environment variables:

• MCP_HOST: host to bind to (defaults to 0.0.0.0). Set to 127.0.0.1 for local development to follow MCP security best practices.
• MCP_PORT: port for the MCP HTTP server (defaults to 8000 when unset).
• DATAGOUV_ENV: prod (default) or demo. This controls which data.gouv.fr environement it uses the data from (https://www.data.gouv.fr or https://demo.data.gouv.fr). By default the MCP server talks to the production data.gouv.fr. Set DATAGOUV_ENV=demo if you specifically need the demo environment.

You will need uv to install dependencies and run the server.

1. Install dependencies

uv sync

2. Prepare the environment file

Copy the example environment file to create your own .env file:

cp .env.example .env

Then optionally edit .env and set the variables that matter for your run:

MCP_HOST=127.0.0.1  # (defaults to 0.0.0.0, use 127.0.0.1 for local dev)
MCP_PORT=8007  # (defaults to 8000 when unset)
DATAGOUV_ENV=prod  # Allowed values: demo | prod (defaults to prod when unset)

Load the variables with your preferred method, e.g.:

set -a && source .env && set +a

3. Start the HTTP MCP server

uv run main.py

Follow the steps in Connect your chatbot to the MCP server and simply swap the hosted URL for your local endpoint (default: http://127.0.0.1:${MCP_PORT:-8000}/mcp).

The MCP server is built using the official Python SDK for MCP servers and clients and uses the Streamable HTTP transport only.

STDIO and SSE are not supported.

Streamable HTTP transport (standards-compliant):

• POST /mcp - JSON-RPC messages (client → server)
• GET /health - Simple JSON health probe ({"status":"ok","timestamp":"..."})

The MCP server provides tools to interact with data.gouv.fr datasets and dataservices.

Note: "Dataservices" are external third-party APIs (e.g., Adresse API, Sirene API) registered in the data.gouv.fr catalog. They are distinct from data.gouv.fr's own internal APIs (Main/Tabular/Metrics) which power this MCP server.

• search_datasets - Search for datasets by keywords. Returns datasets with metadata (title, description, organization, tags, resource count).

  Parameters: query (required), page (optional, default: 1), page_size (optional, default: 20, max: 100)

• get_dataset_info - Get detailed information about a specific dataset (metadata, organization, tags, dates, license, etc.).

  Parameters: dataset_id (required)

• list_dataset_resources - List all resources (files) in a dataset with their metadata (format, size, type, URL).

  Parameters: dataset_id (required)

• get_resource_info - Get detailed information about a specific resource (format, size, MIME type, URL, dataset association, Tabular API availability).

  Parameters: resource_id (required)

• query_resource_data - Query data from a specific resource via the Tabular API. Fetches rows from a resource to answer questions.

  Parameters: question (required), resource_id (required), page (optional, default: 1), page_size (optional, default: 20, max: 200)

  Note: Recommended workflow: 1) Use search_datasets to find the dataset, 2) Use list_dataset_resources to see available resources, 3) Use query_resource_data with default page_size (20) to preview data structure. For small datasets (<500 rows), increase page_size or paginate. For large datasets (>1000 rows), use download_and_parse_resource instead. Works for CSV/XLS resources within Tabular API size limits (CSV ≤ 100 MB, XLSX ≤ 12.5 MB).

• download_and_parse_resource - Download and parse a resource that is not accessible via Tabular API (files too large, formats not supported, external URLs).

  Parameters: resource_id (required), max_rows (optional, default: 20), max_size_mb (optional, default: 500)

  Supported formats: CSV, CSV.GZ, JSON, JSONL. Useful for files exceeding Tabular API limits or formats not supported by Tabular API. Start with default max_rows (20) to preview, then call again with higher max_rows if you need all data.

• search_dataservices - Search for dataservices (APIs) registered on data.gouv.fr by keywords. Returns dataservices with metadata (title, description, organization, base API URL, tags).

  Parameters: query (required), page (optional, default: 1), page_size (optional, default: 20, max: 100)

• get_dataservice_info - Get detailed metadata about a specific dataservice (title, description, organization, base API URL, OpenAPI spec URL, license, dates, related datasets).

  Parameters: dataservice_id (required)

• get_dataservice_openapi_spec - Fetch and summarize the OpenAPI/Swagger specification for a dataservice. Returns a concise overview of available endpoints with their parameters.

  Parameters: dataservice_id (required)

  Note: Recommended workflow: 1) Use search_dataservices to find the API, 2) Use get_dataservice_info to get its metadata and documentation URL, 3) Use get_dataservice_openapi_spec to understand available endpoints and parameters, 4) Call the API using the base_api_url per the spec.

• get_metrics - Get metrics (visits, downloads) for a dataset and/or a resource.

  Parameters: dataset_id (optional), resource_id (optional), limit (optional, default: 12, max: 100)

  Returns monthly statistics including visits and downloads, sorted by month in descending order (most recent first). At least one of dataset_id or resource_id must be provided. Note: This tool only works with the production environment (DATAGOUV_ENV=prod). The Metrics API does not have a demo/preprod environment.

Run the tests with pytest (these cover helper modules; the MCP server wiring is best exercised via the MCP Inspector):

# Run all tests
uv run pytest

# Run with verbose output
uv run pytest -v

# Run specific test file
uv run pytest tests/test_tabular_api.py

# Run with custom resource ID
RESOURCE_ID=3b6b2281-b9d9-4959-ae9d-c2c166dff118 uv run pytest tests/test_tabular_api.py

# Run with prod environment
DATAGOUV_ENV=prod uv run pytest

Use the official MCP Inspector to interactively test the server tools and resources.

Prerequisites:

• Node.js with npx available

Steps:

1. Start the MCP server (see above)
2. In another terminal, launch the inspector:

   npx @modelcontextprotocol/inspector --http-url "http://127.0.0.1:${MCP_PORT}/mcp"

   Adjust the URL if you exposed the server on another host/port.

We welcome contributions! To keep the project stable and reviews manageable, please observe these rules before submitting:

• Keep it small: We strictly follow a 1 feature = 1 PR workflow.
• Human review required: Do not submit raw AI-generated code. All code must be reviewed and tested by a human prior to submission.

We use a standard review-and-deploy process:

1. Submit a PR: Propose your changes via a Pull Request against the main branch.
2. Review: All PRs must be reviewed and approved by a maintainer before merging.
3. Automated Deployment: Once merged into main, changes will be deployed to:
   1. Pre-production for final validation
   2. Production

This project follows PEP 8 style guidelines using Ruff for linting and formatting, and ty for type checking.

Either running these commands manually or installing the pre-commit hook is required before submitting contributions.

# Lint (including import sorting) and format code
uv run ruff check --fix && uv run ruff format

# Type check (ty)
uv run ty check

This repository uses a pre-commit hook which lint and format code before each commit. Installing the pre-commit hook is strongly recommended so the checks run automatically.

Install pre-commit hooks:

uv run pre-commit install

The pre-commit hook that automatically:

• Check YAML syntax
• Fix end-of-file issues
• Remove trailing whitespace
• Check for large files
• Run Ruff linting and formatting

The release process uses the tag_version.sh script to create git tags, GitHub releases and update CHANGELOG.md automatically. Package version numbers are automatically derived from git tags using setuptools_scm, so no manual version updates are needed in pyproject.toml.

Prerequisites: GitHub CLI must be installed and authenticated, and you must be on the main branch with a clean working directory.

# Create a new release
./tag_version.sh <version>

# Example
./tag_version.sh 2.5.0

# Dry run to see what would happen
./tag_version.sh 2.5.0 --dry-run

The script automatically:

• Extracts commits since the last tag and formats them for CHANGELOG.md
• Identifies breaking changes (commits with !: in the subject)
• Creates a git tag and pushes it to the remote repository
• Creates a GitHub release with the changelog content

This project is licensed under the MIT License - see the LICENSE file for details.