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france-data-mcp

by io.github.cturkieh

panorama_sante_territoire

Read-onlyIdempotent

Panorama santé d'une commune française en 1 appel (V0.9). Agrège en parallèle : population (INSEE Melodi), densités médecins + infirmiers + pharmaciens avec comparaison nationale (méthodo DREES), nombre d'établissements FINESS par famille (default ["labo","pharmacie","ehpad","mco","msp_cpts"]), et un bloc DEMANDE (V0.22.0 — profil démographique de la commune agrégé depuis ses IRIS : âge, CSP, familles, revenu pondéré, à CROISER avec l'OFFRE ci-dessus pour l'aide à l'implantation ; demande: null si commune hors couverture IRIS (DOM non ingéré) — pour le détail au quartier ou un bassin par rayon, utiliser profil_iris).

Remplace 7-10 appels MCP individuels par 1 seul. Ne renvoie AUCUNE interprétation métier (pas de qualification automatique 'désert médical') — le caller LLM applique sa grille.

V0.19.0 : accepte nom_commune (string) comme alternative à code_insee. departement (V0.19) = hint resolver UNIQUEMENT (panorama ne calcule pas par dept ; un departement seul lève une erreur explicite).

Granularité mixte : les densités professionnels et la population sont calculées au niveau commune ; le décompte FINESS est agrégé au niveau département dérivé du code INSEE (limitation V0.9 — pas de RPC count_finess_by_commune encore). Le champ niveauEtablissements du résultat indique "departement" (succès), "indisponible" (dept indérivable, ex code DOM tronqué) — utiliser cette information pour ne pas confondre ratios commune et dept.

Paris/Marseille/Lyon NON supporté : le panorama par commune dépend de la densité par commune, indisponible pour ces villes (INSEE n'expose la population qu'à la commune entière, les praticiens RPPS aux arrondissements). Un code PLM (commune-mère 75056 ou arrondissement) lève une RangeError. Pour ces villes, interroger les tools individuels au niveau code_dept (75/69/13).

Alias acceptés : codeInsee/insee/code → code_insee.

Sources : RPPS / Annuaire Santé ANS (mensuel), FINESS DREES (bimensuel), INSEE Melodi (PMUN 2023).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
code_inseeNoCode INSEE de la commune 5 caractères. Ex: "59009" Villeneuve-d'Ascq, "33063" Bordeaux, "2A004" Ajaccio. Paris/Lyon/Marseille NON supporté (voir description). XOR avec `nom_commune`.
departementNoCode département INSEE (V0.19, hint resolver UNIQUEMENT). À utiliser EN COMBINAISON avec `nom_commune` pour désambiguer les homonymes. Seul, lève une erreur (panorama = calcul commune uniquement, utiliser `code_insee` ou `nom_commune`).
nom_communeNoNom officiel de commune (alternative à `code_insee`, V0.19). Ex: "Lille", "Saint-Étienne". Combinable avec `departement` comme hint de désambiguïsation pour homonymes (ex "Saint-Martin" + dept "65"). Abréviations type "St-Martin" non reconnues.
finess_famillesNoFamilles FINESS à inclure dans le décompte établissements. Default ["labo","pharmacie","ehpad","mco","msp_cpts"]. Passer [] pour omettre le décompte FINESS (renvoie uniquement population + densités PS).

TDQS

A4.9/5.0
Behavior5/5

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

Description adds significant behavioral context: returns no business interpretation (no 'désert médical'), granularity mix (commune vs department), PLM cities unsupported, 'demande' block null for DOM communes, and sources/update frequency. Annotations already indicate readonly, idempotent, non-destructive, but description enriches with these details.

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?

Description is long but well-structured: summary first, then version updates, granularity mix, PLM warning, aliases, sources. Could be slightly more concise (version numbers may be unnecessary for agent), but front-loaded 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?

Given complexity (multiple data sources, mixed granularity, special cases) and no output schema, description covers result blocks (population, densities, FINESS, demande) and null cases, error conditions (PLM, departement alone), and version behavior. It is complete enough for agent to use effectively.

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

Parameters5/5

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

Schema coverage is 100%, but description adds meaning: 'code_insee' notes PLM unsupported, 'departement' is hint only, 'nom_commune' combinable with departement, no abbreviations, 'finess_familles' explains default and effect of empty array. These details go beyond schema.

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 aggregates multiple data sources into one call for a French commune, replacing 7-10 individual MCP calls. It distinguishes from siblings like 'profil_iris' and 'densite_sante' by providing a holistic overview.

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 states when to use (single commune, replaces many calls) and when not to (Paris/Lyon/Marseille not supported, use individual tools at department level). Also explains parameter usage: 'departement' as hint only, error if used alone; granularity mix warns against confusing commune and department ratios.

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.

Resources