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Glama

france-data-mcp

by io.github.cturkieh

etablissement_by_finess

Read-onlyIdempotent

Récupère le détail complet d'un établissement de santé par son numéro FINESS (9 chiffres) : raison sociale, catégorie + famille, adresse complète (voie + CP + ville + code INSEE + département), coordonnées GPS, téléphone. Retourne un objet LookupResult discriminé par found. found: true → champs FINESS à plat. found: false → { found: false, key, lookupStatus: 'not_found', message }. Le référentiel DREES a 1-2 mois de retard sur le terrain : pour des structures émergentes (CPTS récentes, MSP en agrément), cross-check ARS / Service Public. Source : FINESS / DREES. Note : champ email toujours null (non exposé par FINESS public). Note : raison_sociale provient du dump DREES qui abrège les libellés longs (~38 car. max, ex 'CERBALLIANCE HA' pour 'CERBALLIANCE HAZEBROUCK'). Pour le nom légal complet, cross-check via SIREN/SIRET (entreprise_by_siren / etablissement_by_siret).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
num_finessYesNuméro FINESS exact (9 chiffres).
include_freshnessNoSi true, ajoute un champ `data_freshness` au payload (dans `query_metadata` si présent, sinon à la racine) listant la dernière ingestion réussie par source (FINESS, Ameli, RPPS, CDS) avec `staleness_days`. Opt-in pour ne pas alourdir les payloads par défaut. Cache 5min côté serveur — coût négligeable.

Output Schema

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

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds: data freshness delay, truncation of raison_sociale (~38 chars), email always null. No contradictions; adds valuable behavioral context beyond 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?

Description is front-loaded with main purpose, but the single paragraph includes multiple notes that could be structured (e.g., bullet points). Still concise given the amount of information, with no wasted sentences.

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?

Output schema exists, annotations are present, description covers return format, data freshness caveat, truncation issue, email null, and cross-references. Complete for a lookup tool with 2 parameters.

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

Parameters3/5

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

Schema coverage is 100% with clear parameter descriptions. Description adds minimal extra meaning: explains include_freshness as opt-in with negligible cost, but does not expand on num_finess. Baseline 3 is appropriate as schema does the heavy lifting.

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?

Description clearly states it retrieves complete details of a healthcare establishment by FINESS number, listing fields (raison sociale, category, address, GPS, phone) and return type (LookupResult discriminated by found). Distinguishes from siblings like etablissements_finess_by_categorie.

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 DREES repository has 1-2 month delay, recommends cross-checking with ARS for emerging structures (CPTS, MSP) and via SIREN/SIRET for full legal name. Provides concrete when-to-use and limitations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All france-data-mcp toolsInspect
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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