datagouv-mcp
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.
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datagouv-mcp is a Model Context Protocol (MCP) server designed to facilitate AI chatbots (such as Claude, ChatGPT, Gemini) in searching, exploring, and analyzing datasets from data.gouv.fr, the French national Open Data platform, through conversation. Users can 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' to get instant answers without manually browsing the website. The server provides tools to interact with datasets and dataservices, supporting features like searching datasets, getting dataset information, listing resources, querying resource data, and more. It also offers support for various chatbots like ChatGPT, Claude Desktop, Claude Code, Gemini CLI, Mistral Vibe CLI, AnythingLLM, VS Code, Cursor, Windsurf, and provides detailed instructions for connecting chatbots to the server.
README:
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.
[!TIP] 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.
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:
Available for paid plans only (Plus, Pro, Team, and Enterprise).
-
Access Settings: Open ChatGPT in your browser, go to
Settings, thenApps and connectors. -
Enable Dev Mode: Open
Advanced settingsand enable Developer mode. -
Add Connector: Return to
Settings>Connectors>Browse connectorsand click Add a new connector. -
Configure the connector: Set the URL to
https://mcp.data.gouv.fr/mcpand save to activate the tools.
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"
]
}
}
}Use the claude mcp command to add the MCP server:
claude mcp add --transport http datagouv https://mcp.data.gouv.fr/mcpAdd 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.
-
Locate the
anythingllm_mcp_servers.jsonfile 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
-
Mac:
-
Add the following configuration:
{
"mcpServers": {
"datagouv": {
"type": "streamable",
"url": "https://mcp.data.gouv.fr/mcp"
}
}
}For more details, see the AnythingLLM MCP documentation.
Add the following to your VS Code settings.json:
{
"servers": {
"datagouv": {
"url": "https://mcp.data.gouv.fr/mcp",
"type": "http"
}
}
}Cursor supports MCP servers through its settings. To configure the server:
- Open Cursor Settings
- Search for "MCP" or "Model Context Protocol"
- Add a new MCP server with the following configuration:
{
"mcpServers": {
"datagouv": {
"url": "https://mcp.data.gouv.fr/mcp",
"transport": "http"
}
}
}IBM Bob supports MCP servers through its settings. To configure the server:
- Click the setting icon in the Bob panel.
- Select the MCP tab.
- Click the appropriate button:
- Edit Global MCP: Opens the global
mcp_settings.jsonfile - Edit Project MCP: Opens the project-specific
.bob/mcp.jsonfile (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 ~/.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 [email protected]:datagouv/datagouv-mcp.git
cd datagouv-mcpDocker 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 downEnvironment variables:
-
MCP_HOST: host to bind to (defaults to0.0.0.0). Set to127.0.0.1for local development to follow MCP security best practices. -
MCP_PORT: port for the MCP HTTP server (defaults to8000when unset). -
DATAGOUV_ENV:prod(default) ordemo. 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. SetDATAGOUV_ENV=demoif you specifically need the demo environment.
You will need uv to install dependencies and run the server.
- Install dependencies
uv sync- Prepare the environment file
Copy the example environment file to create your own .env file:
cp .env.example .envThen 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- Start the HTTP MCP server
uv run main.pyFollow 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_datasetsto find the dataset, 2) Uselist_dataset_resourcesto see available resources, 3) Usequery_resource_datawith defaultpage_size(20) to preview data structure. For small datasets (<500 rows), increasepage_sizeor paginate. For large datasets (>1000 rows), usedownload_and_parse_resourceinstead. 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_dataservicesto find the API, 2) Useget_dataservice_infoto get its metadata and documentation URL, 3) Useget_dataservice_openapi_specto understand available endpoints and parameters, 4) Call the API using thebase_api_urlper 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_idorresource_idmust 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 pytestUse the official MCP Inspector to interactively test the server tools and resources.
Prerequisites:
- Node.js with
npxavailable
Steps:
- Start the MCP server (see above)
- In another terminal, launch the inspector:
Adjust the URL if you exposed the server on another host/port.
npx @modelcontextprotocol/inspector --http-url "http://127.0.0.1:${MCP_PORT}/mcp"
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:
-
Submit a PR: Propose your changes via a Pull Request against the
mainbranch. - Review: All PRs must be reviewed and approved by a maintainer before merging.
-
Automated Deployment: Once merged into
main, changes will be deployed to:- Pre-production for final validation
- 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 checkThis 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 installThe 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-runThe 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.
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