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Atlas Scolaire

by DanyVilela
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Atlas Scolaire — MCP server

2025 brevet and baccalauréat results for the 10,098 collèges and lycées of France métropolitaine, served to AI assistants over the Model Context Protocol — with one rule built into every response: a school's success index never travels without its social intake (IPS), the intake's spread, and the ministry's value-added. Read alone, the success index measures who a school admits more than what it does.

The data comes from atlas-scolaire.fr, which joins six open datasets — five published by the French education ministry, plus the Base adresse nationale — under Licence Ouverte 2.0.

Connect

  • Endpoint: https://mcp.atlas-scolaire.fr/mcp (Streamable HTTP, no authentication)

  • Claude.ai / Claude Desktop: Settings → Connectors → Add custom connector, then paste the endpoint.

  • Claude Code: claude mcp add --transport http atlas-scolaire https://mcp.atlas-scolaire.fr/mcp

Related MCP server: mcp-french-school-map

Tools

Tool

What it returns

search_schools

Identities only (UAI, name, commune, page): many schools share a name.

get_school

One school, results (each with its national percentile) with intake and value-added.

compare_schools

2–5 schools in the order given, no computed winner.

schools_in_area

A commune or département, 25 per page (page), ranked by success index within each exam by default; sort also takes value_added (results net of the intake) or name. Filters: type, sector, exam (only schools with a published result for it, and the exam the ranking uses), teaching options.

schools_near

Schools near an address or a point, nearest first by default, 25 per page; sort also takes success_index, value_added or name. Same filters.

similar_intake_schools

Schools of the same type with a comparable IPS, nearest first, up to 10.

area_overview

Area aggregates, each result median paired with its median IPS.

A response that holds several schools states the reading note (how_to_read) and the attribution once, at its top level; get_school carries both on its single record.

Plus two resources (atlas://methodology, atlas://sources) and one prompt (choose_a_school).

Lists can be ordered by results, as the site's own tables are, one exam at a time: a list ordered by success index or value-added gives each school its rank among the schools ranked on the same exam (ranked_on) — ranks restart for each exam, and no rank compares a brevet with a bac, or one bac with another — and every exam carries the school's national success_index_percentile. What never happens is a result travelling alone: every ranked school carries its IPS, the IPS standard deviation and its value-added in the same record.

Evaluation

A pilot comparing four conditions (no tools, web, web + a naive server with the same data, web + this server) is written up in evals/RESULTS.md, with the plan in evals/PLAN.md.

Data and licence

Data: /donnees/etablissements.v1.json, Licence Ouverte 2.0 — the six sources are listed at atlas-scolaire.fr/#sources. Code: MIT.

Development

npm install
npm test            # unit tests over a hand-built fixture: no data file, no network
LIVE=1 npm test     # also downloads and indexes the live data file
npm run check       # tsc
npm run fetch-data  # download the data file into data/ (git-ignored)
npm run dev         # fetch-data, then wrangler dev on http://localhost:8787/mcp
npm run deploy      # fetch-data, then wrangler deploy

The data file is not fetched at request time: npm run fetch-data downloads it, checks its version, and wrangler bundles it into the Worker, which builds its index once at start-up. A data refresh on the site therefore reaches this server only through a redeploy — npm run deploy downloads the current file first.

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Maintenance

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Resources

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