data.gouv.fr MCP Server

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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.

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Use it now: A public instance is available for everyone at https://mcp.data.gouv.fr/mcp with no access restrictions.
To connect your favorite chatbot, simply follow the connection instructions below.

🌐 Connect your chatbot to the MCP server

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 | HuggingChat | IBM Bob | Kiro CLI | Kiro IDE | Le Chat (Mistral) | Mistral Vibe | OpenCode | VS Code | Windsurf

AnythingLLM

1. Locate the anythingllm_mcp_servers.json file in your AnythingLLM storage plugins directory:
   • Linux: ~/.config/anythingllm-desktop/storage/plugins/anythingllm_mcp_servers.json
   • MacOS: ~/Library/Application Support/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.

ChatGPT

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.

Claude Code

Use the claude mcp command to add the MCP server:

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

Claude Desktop

Add the following to your Claude Desktop configuration file (typically ~/.config/Claude/claude_desktop_config.json on Linux, ~/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"
      ]
    }
  }
}

Claude Desktop on Windows: If the server appears in the list but never connects (no handshake, tools missing), Claude may be using its built-in Node.js runtime, which does not see packages installed with your system npm (including a global mcp-remote). Set isUsingBuiltInNodeForMcp to false at the root of the same config file so npx uses your installed Node — then restart Claude Desktop:

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

See issue #69 for background.

Cursor

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"
    }
  }
}

Gemini CLI

Add the following to your ~/.gemini/settings.json file (Linux: ~/.gemini/settings.json, MacOS: ~/.gemini/settings.json, Windows: %USERPROFILE%\.gemini\settings.json):

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

HuggingChat

1. Access Settings: In the chat interface, click the + icon, select MCP Servers, and click Manage MCP Servers.
2. Add Server: Click the + Add Server button in the server management window.
3. Configure the Server: Enter a Server Name (e.g., "Data Gouv") and set the Server URL to https://mcp.data.gouv.fr/mcp. Click Add Server to save.
4. Verify Connection: Click the Health Check button on the new server card to confirm it displays as Connected. Ensure the toggle is activated to use the tools in your chat.

IBM Bob

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"
    }
  }
}

Kiro CLI

Add the following to ~/.kiro/settings/mcp.json (Linux: ~/.kiro/settings/mcp.json, MacOS: ~/.kiro/settings/mcp.json, Windows: %USERPROFILE%\.kiro\settings\mcp.json):

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

Kiro IDE

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

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

Le Chat (Mistral)

Available on all plans, including free.

1. Go to Connectors: Open Mistral in your browser, then go to Intelligence > Connectors.
2. Add a custom connector: Click Add connector > Custom MCP Connector, give it a name (for example DataGouv), and set the server URL to https://mcp.data.gouv.fr/mcp.
3. No authentication: Leave authentication disabled.
4. Create: Click Create.

Mistral Vibe CLI

Edit your Vibe config (default: Linux: ~/.vibe/config.toml, MacOS: ~/.vibe/config.toml, Windows: %USERPROFILE%\.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.

OpenCode

Add to opencode.json (e.g. ~/.config/opencode/opencode.json or your project root). Remote servers use the top-level mcp object with type: "remote". See OpenCode MCP servers.

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "datagouv": {
      "type": "remote",
      "url": "https://mcp.data.gouv.fr/mcp",
      "enabled": true
    }
  }
}

VS Code

Add the following to your VS Code mcp.json file (Linux: ~/.config/Code/User/mcp.json, MacOS: ~/Library/Application Support/Code/User/mcp.json, Windows: %APPDATA%\Code\User\mcp.json). Run MCP: Open User Configuration from the Command Palette to open it.

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

Windsurf

Add the following to your ~/.codeium/windsurf/mcp_config.json (Linux: ~/.codeium/windsurf/mcp_config.json, MacOS: ~/.codeium/windsurf/mcp_config.json, Windows: %USERPROFILE%\.codeium\windsurf\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.

🖥️ Run locally

1. Run the MCP server

Before starting, clone this repository and browse into it:

git clone [email protected]: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 Docker (Recommended)

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

# With custom environment variables
MCP_PORT=8007 DATAGOUV_API_ENV=demo LOG_LEVEL=DEBUG 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).
• MCP_ENV: environment name reported to Sentry (defaults to local when unset). Set explicitly to prod, preprod, or demo in your deployment.
• DATAGOUV_API_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_API_ENV=demo if you specifically need the demo environment.
• LOG_LEVEL: Python logging level for the application (defaults to INFO). Common values: DEBUG, INFO, WARNING, ERROR, CRITICAL.
• SENTRY_DSN: Sentry DSN to enable error and performance monitoring. Monitoring is disabled when unset.
• SENTRY_SAMPLE_RATE: sampling rate for Sentry traces and profiles (float 0.0–1.0, defaults to 1.0).

⚙️ Manual Installation

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)
MCP_ENV=local  # environment name sent to Sentry (defaults to local when unset)
DATAGOUV_API_ENV=prod  # Allowed values: demo | prod (defaults to prod when unset)
LOG_LEVEL=INFO  # Python log level (default: INFO)

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

2. Connect your chatbot to the local MCP server

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).

🚚 Transport support

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.

📋 Available Endpoints

Streamable HTTP transport (standards-compliant):

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

🛠️ Available Tools

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.

Datasets (static data files)

• 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: 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), continue paginating or use get_resource_info to retrieve the raw file URL and fetch it directly. Works for CSV/XLS resources within Tabular API size limits (CSV ≤ 100 MB, XLSX ≤ 12.5 MB).

Dataservices (external APIs)

• 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.

Metrics

• 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_API_ENV=prod). The Metrics API does not have a demo/preprod environment.

🧪 Tests

✅ Automated Tests with pytest

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_API_ENV=prod uv run pytest

🔥 Stress Tests

Stress tests send many concurrent requests against a running MCP server. They require a running server and make real HTTP requests, so they are excluded from default pytest runs.

# Start the server first, then in another terminal:
uv run pytest -m stress

Currently includes a test that mixes normal requests with abrupt client TCP disconnects, verifying the server stays healthy and keeps serving despite the disruption. It uses MCP_PORT (default: 8000) to connect to the local server.

🔍 Interactive Testing with MCP Inspector

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.

🤝 Contributing

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

• Human review and accountability: Do not submit raw, unreviewed AI-generated code. Every change must be read, understood, tested, and validated by a human before you open a PR. By submitting a pull request, you certify that you fully understand the proposed code and could explain and defend it in review without relying on an AI assistant.
• Keep it small: We strictly follow a 1 feature = 1 PR workflow.
• Conventional commits: Use the Conventional Commits format for git commit messages and PR titles (e.g. feat: add dataset search, fix: handle empty API response). See the specification for allowed types, scopes, and breaking-change markers.

We use a standard review-and-deploy process:

1. Submit a PR: Propose your changes via a Pull Request against the main branch.
2. Continuous integration: CI runs automatically on the pull request. All required checks must pass before the PR can be merged (tests, linting, formatting, and type checking). Run the same checks locally—tests per Tests, and lint/format/type via Code linting and formatting or the pre-commit hook—to avoid surprise CI failures.
3. Review: All PRs must be reviewed and approved by a maintainer before merging.
4. Deployment process: Once merged into main, maintainers deploy changes periodically to pre-production for more tests and validation before wider release.

🧹 Code Linting and Formatting

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

🔗 Pre-commit Hooks

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

🏷️ Releases and versioning

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

📄 License

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