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cturkieh

France Data MCP

by cturkieh
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

population

Read-onlyIdempotent

Retrieve resident counts for French communes, departments, or IRIS zones by INSEE code. Granularity is auto-detected from 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?

Description adds significant behavioral details beyond annotations: auto-detection of granularity by code length, return structure (LookupResult with found field), edge cases (fusion -> found:false, Mayotte absent), and data sources. 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 long but well-structured with clear sections for each granularity. It is front-loaded with the core purpose. Every sentence provides useful information, justifying the length for a complex tool.

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?

The description covers all code types, returns structure, data sources, and edge cases. Given the output schema exists (not shown), the description is complete and provides sufficient context for correct tool usage.

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 100%, and the description enriches the parameter meaning by explaining auto-detection, code length mappings, aliases, and examples. This adds value beyond the schema description, so it earns a 4.

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 retrieves population data for commune, département, or IRIS based on INSEE code length. It distinguishes from siblings like profil_iris and autocomplete_commune, specifying that this tool is for population counts while profil_iris provides detailed demographic profiles.

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?

Explicit guidance on when to use (population totals) and when not (detailed demographics use profil_iris; fused communes use autocomplete_commune). It also notes that PLM arrondissements and Mayotte are not supported, and gives alternative handling.

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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