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cturkieh

France Data MCP

profil_iris

Read-onlyIdempotent

Get demographic profile at neighborhood level (IRIS) for a point or area. Returns age, socio-professional categories, family types, and income data to analyze demand for site selection.

Instructions

Profil démographique au grain QUARTIER (IRIS) — la « demande » d'un territoire (âge, CSP, familles, revenu), à croiser avec l'offre de soins pour l'aide à l'implantation. Source : INSEE RP 2022 + FILOSOFI 2021 (tables ingérées, géo 01/01/2024). Retourne un LookupResult discriminé par found.

Entrée : EXACTEMENT un de point (lat+lon) OU code_iris (9 car.). rayon_km optionnel (0 < r ≤ 10) → DEUX modes :

  • SANS rayon_km → profil de l'ÎLOT seul (~2000 hab) sous le point / du code. mode: "ilot", revenu_median = médiane réelle de l'îlot.

  • AVEC rayon_km → AGRÉGAT du BASSIN = îlots dont le CENTROÏDE est dans le disque (chaque îlot compté 1 fois). mode: "bassin", population_bassin, nb_iris_agreges, et revenu_median_pondere = PROXY (moyenne pondérée population des médianes des îlots couverts — PAS une vraie médiane de bassin) + couverture {revenu_pct_population, iris_revenu_manquants} car FILOSOFI ne couvre que les communes ≥5000 hab.

Les parts age (part_65_plus/75_plus) et csp (cadres, prof_interm, employés, ouvriers, agriculteurs, artisans_comm, retraités, autres) sont des ratios sur comptes bruts (Σ/Σ). Pour une simple population de commune/dept, utiliser population. not_found motivé si code absent ou point hors métropole / en mer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude du point (mode point).
lonNoLongitude du point (mode point).
code_irisNoCode IRIS 9 caractères (ex `751103701`) — alternatif au point.
rayon_kmNoRayon du bassin en km (0 < r ≤ 10). Absent = profil de l'îlot seul.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
foundYes
lookupStatusYes
keyNoClé recherchée (SIREN, num_finess, code INSEE, …).
messageNoExplication actionnable quand `found=false` (cause probable + remédiation).
Behavior5/5

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

The description adds significant behavioral context beyond the annotations. It explains that the tool returns a LookupResult discriminated by 'found', details the aggregation logic for the basin mode, the proxy income calculation, and the coverage limitations of the FILOSOFI source. There is no contradiction with annotations.

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

Conciseness4/5

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

The description is fairly long but well-structured, with a clear summary of two modes and bullet-like details. Every sentence adds value, though it could be slightly more concise without losing information.

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?

Given the tool's complexity (4 parameters, two modes, aggregation logic, proxy calculation, coverage caveats), the description is remarkably complete. The output schema handles return values, and the description adds context about 'not_found' conditions and where the data comes from (INSEE RP 2022, FILOSOFI 2021).

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?

Although schema description coverage is 100%, the description adds substantial meaning beyond the schema: it requires exactly one of point (lat+lon) or code_iris, explains the two modes controlled by rayon_km, and clarifies the format and purpose of code_iris. This adds value beyond the baseline of 3.

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 description clearly states the tool provides a demographic profile at the IRIS (neighborhood) level, combining demand data (age, CSP, families, income) with healthcare supply for implantation aid. It uses specific verbs and resources, and the purpose is distinct from sibling tools like 'population' and 'get_commune_by_code'.

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?

The description explicitly explains two modes (with and without rayon_km), when to use each, and what they return. It also provides an alternative: 'Pour une simple population de commune/dept, utiliser `population`.' This gives clear when-to-use and when-not-to-use guidance.

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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