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cturkieh

France Data MCP

by cturkieh
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

population

Read-onlyIdempotent

Look up population totals for French communes, departments, or IRIS areas using an INSEE code; the geographic level is automatically determined from the code length.

Instructions

Population d'une COMMUNE (code INSEE 5 car.), d'un DÉPARTEMENT (2-3 car.) OU d'un IRIS infracommunal (9 car.) — granularité auto-détectée par la longueur du code. Retourne un LookupResult discriminé par found.

  • IRIS (9 car., ex 751103701 = commune 75110 + IRIS 3701) : population totale du quartier au Recensement 2022 (champ population, comptes bruts), + libelle, code_commune, type_iris (H/A/D/Z). Source : INSEE RP 2022 (table ingérée, géo 01/01/2024). Maille la plus fine (quartier) pour les villes ; en zone peu dense la commune = 1 IRIS (type_iris Z, code COM+0000). Pour le profil démographique détaillé d'un îlot ou d'un bassin (âge, CSP, familles, revenu), utiliser profil_iris.

  • Commune (5 car., ex 75056 Paris, 13055 Marseille, 2A004 Ajaccio) : PMUN/PCAP/PTOT. Source INSEE Melodi (DS_POPULATIONS_REFERENCE). PMUN = base légale DREES. Commune fusionnée → found: false + orientation autocomplete_commune. INSEE n'expose PAS les arrondissements PLM (75101-75120, 13201-13216, 69381-69389) → passer la commune-mère ou le département.

  • Département (2-3 car., ex 75, 59, 2A, 971) : Mayotte (976) ABSENTE de Melodi → lookupNotFound.

Alias acceptés : code_insee/codeInsee/insee, code_dept/dept/departement/code_departement, code_iris/iris → code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesCode INSEE — 5 caractères = commune (ex "75056"), 2-3 caractères = département (ex "75", "971", "2A"). Granularité auto-détectée par la longueur.

Output Schema

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

Tool Definition Quality

A4.8/5.0
Behavior5/5

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

Adds significant behavioral context beyond annotations: returns LookupResult discriminated by found, explains data sources, special cases (PLM arrondissements, Mayotte), and output fields for each granularity. Annotations already indicate readOnly and idempotent, but description enriches.

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 and front-loaded, but slightly long. Every sentence adds value; could be trimmed slightly but still effective.

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 expected use cases, error conditions, and orientation. With output schema present, description doesn't need to detail return values. Complete for a multi-granularity data retrieval tool.

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?

Input schema already describes code well (100% coverage), but description adds aliases, edge cases, and granularity examples. Provides extra value beyond schema, though schema alone is sufficient.

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?

Clearly states the tool returns population data for communes, departments, or IRIS, auto-detecting granularity by code length. Identifies siblings like profil_iris and autocomplete_commune, differentiating when each should be used.

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 tells when to use this tool vs alternatives: 'Pour le profil démographique détaillé ... utiliser profil_iris' and for commune fusionnée suggests autocomplete_commune. Also notes Mayotte absence leading to lookupNotFound.

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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