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.
Runssource: GitHubhomepagePythonMITcommit 5a37d6f0f1da
Python, MIT licensed. The project labels itself: mcp, mcp server, open data and opendata.
datagouv-mcp runs. An Argusic agent installed it in 1 minute and hit 1 error and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Python 3.12 environment with relaxed requires-python; package installs and builds; all 147 tests pass; MCP server starts and responds HTTP 200 on /health
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 100 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- requires-python=>=3.13,<3.15 but only Python 3.12 available1 minute
- Install time
- 1 minutes
- Cold machine to finish
- 5 minutes
- Errors hit and fixed
- 1 hit, 1 fixed with no human help
- How the result was proved
- pytest ran 147 passed, 2 deselected. Server started, curl http://localhost:8000/health returned 200.
- Model tokens used
- 49,829
- Exact commit tested
- 5a37d6f0f1da
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
true' in /work/repo The server launched, the health endpoint returned HTTP 200, and the MCP tool operations ran correct… **Errors encountered:** - Python version requirement was `>=3.13,<3.15` but only Python 3.12 is available in the container.… **Verified state:** All 147 tests pass. The server starts and responds with HTTP 200 on the `/healt… 2026-10-06T19:21:16.948394Z ERROR codex_core::session: failed to record rollout items: thread 01a11… tokens used 49,829 The server launched, the health endpoint returned HTTP 200, and the MCP tool operations ran correct… **Errors encountered:** - Python version requirement was `>=3.13,<3.15` but only Python 3.12 is available in the container.… **Verified state:** All 147 tests pass. The server starts and responds with HTTP 200 on the `/healt…
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Installed in 1 minute, faster than the median of the 12 comparable projects Argusic has measured.
- Recovered from all 1 error without a human stepping in, which says the failures are documented well enough to solve.
- Ran without a GPU, so it does not need one to start.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 13 mcp projects Argusic has installed and timed, datagouv-mcp was the 3rd fastest to reach a running state, and 11 of 13 reached one at all.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
- A quick evaluation: it was running 1 minutes after the clone.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
Topics (from GitHub)
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/datagouv-mcp)Questions
- Does datagouv-mcp run?
- Yes. datagouv-mcp runs. Argusic installed and launched it on a clean machine in 1 minutes, hitting 1 error on the way, and recorded the session.
- How did Argusic test datagouv-mcp?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 5a37d6f0f1da. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test datagouv-mcp?
- The run that produced this verdict cost $0.02: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does datagouv-mcp take to install?
- 1 minute in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does datagouv-mcp need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing datagouv-mcp?
- 1 thing broke in the recorded run, and 1 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does datagouv-mcp compare with the alternatives?
- Of the 13 mcp projects Argusic has installed and timed, datagouv-mcp was the 3rd fastest to reach a running state, and 11 of 13 reached one at all.
- Where is the evidence for datagouv-mcp?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.