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france-data-mcp

by io.github.cturkieh

profil_iris

Read-onlyIdempotent

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

Output Schema

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

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already indicate safe, idempotent read. Description adds that output is a LookupResult discriminated by 'found', details proxy calculation for revenu_median_pondere, data sources (INSEE RP 2022, FILOSOFI 2021), and coverage limitations (communes ≥5000 hab). No contradiction.

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?

Well-structured with clear separation of modes via bullet points. Slightly long but every sentence adds essential information. Front-loaded with purpose.

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?

Covers all aspects: dual modes, input constraints, output structure (LookupResult), data provenance, coverage caveats, and proxy calculations. No missing context despite complexity.

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 covers 100% with concise descriptions. Description adds behavioral context: lat+lon vs code_iris exclusivity, rayon_km modes, and the meaning of computed fields (e.g., revenu_median_pondere as proxy). Exceeds 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 it provides demographic profiles at the IRIS (neighborhood) level for location assistance, distinguishing between single-IRIS and basin modes. It contrasts with siblings like 'population' (simple commune/department counts).

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?

Explicitly explains two modes (with/without rayon_km), specifies input constraints (exactly one of point or code_iris), and advises using 'population' for simpler population needs. Covers invalid cases (not_found).

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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TDQS

A4/5.0
Disambiguation3/5

While tools have distinct purposes, there is overlap among several similar tools (e.g., multiple professional and establishment search tools). The detailed descriptions help differentiate, but an agent may struggle to choose correctly among them.

Naming Consistency2/5

Naming mixes French and English, with no consistent pattern (e.g., 'enrichir_concurrents' vs 'inspect_site', 'etablissement_by_finess' vs 'etablissements_finess_in_radius'). This inconsistency makes the toolset harder to navigate.

Tool Count3/5

36 tools is high but justified given the broad domain. However, there are multiple tools for similar tasks (e.g., four professional search tools), suggesting some redundancy. The scope is borderline but acceptable.

Completeness4/5

The toolset covers a wide range of needs for French health data analysis: establishments, professionals, population, geocoding, demographics, and composite analyses. Few obvious gaps exist, though some specialized tasks might require additional integration.

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