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urba_iris

INSEE census at the IRIS ('quartier') level — finer than a commune.

The IRIS (~2 000 inhabitants) is INSEE's neighbourhood mesh: communes of ≥10 000 inhabitants (and most of 5 000-10 000) are split into IRIS. urba_socio stops at the commune; this drills inside it.

code accepts either:

  • a 5-digit COMMUNE / arrondissement code → returns ALL IRIS of that commune plus commune totals (e.g. '13201' = Marseille 1er, '75112' = Paris 12e) ;

  • a 9-digit IRIS code → returns that single IRIS.

Per IRIS (RP 2021 counts): population (+ age bands 0-19/20-64/65+), dwellings, main/secondary/vacant residences, houses vs flats, and households living in a flat (rp_en_appartement — a proxy for laundromat/shared-service demand). typ_iris: H habitat / A activité / D divers / Z whole undivided commune. Neighbourhood NAMES aren't in this file (lab_iris is INSEE's numeric label). For population TREND per quartier, note this millésime is single-year; use urba_socio at the arrondissement level for evolution.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "code": "<code>"
    },
    "name": "urba_iris"
  }
}

Input Schema

NameRequiredDescriptionDefault
_orgNoOrg (id) de CET APPEL. `oto_list_orgs()`.
codeYes
_groupNoÉquipe (id) de CET APPEL, sous son org parente.
_run_idNoOBLIGATOIRE à chaque appel dès `run_start` ou `data_claim_next` : sans lui, l'écriture sur une ligne réservée est refusée. Hérité seulement si le serveur tient un run actif — n'y compte pas.
_projectNoProjet (id) de CET APPEL — jeton PRIMAIRE. `oto_project(op='list')`.
_instanceNoInstance de connecteur PRÉCISE (un credential exact). `oto_instance(op='list')`.

Schema Changelog

Changes observed during successful MCP inspections.

No schema history has been recorded yet.

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the load and does so well: it discloses the source and vintage (RP 2021 counts), the fields returned per IRIS, the meaning of typ_iris values, and two important caveats (no neighbourhood names, single-year millésime). It does not cover auth/credential requirements or pagination, which keeps it from a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the one-line purpose, then a definition, then a bulleted breakdown of the code formats, then caveats. Dense but every paragraph carries distinct information — no repetition of the schema or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description must describe returns, and it does: population, age bands, dwelling and residence types, houses vs flats, rp_en_appartement, plus commune totals. Combined with the code-format and caveat coverage, an agent has everything needed to call it correctly and interpret the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 83%, so the five infrastructure parameters (_org, _project, _run_id, etc.) are already documented in the schema. The description goes further than the schema on the only substantive parameter, `code`, spelling out both accepted formats and the differing cardinality of the response for each — real meaning added beyond a bare string type.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening line names the exact resource and granularity ('INSEE census at the IRIS level — finer than a commune') and explicitly contrasts with the sibling 'urba_socio stops at the commune; this drills inside it'. An agent can distinguish this from urba_socio without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit routing rules: use a 5-digit commune code to get all its IRIS plus totals, a 9-digit code for a single IRIS, and use urba_socio instead for commune-level or per-quartier population trend. Both positive and negative usage conditions are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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