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cturkieh

France Data MCP

etablissement_by_finess

Read-onlyIdempotent

Retrieve full details of a healthcare establishment using its FINESS number, including legal name, address, GPS coordinates, and category. Returns found/not found status.

Instructions

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
foundYes
lookupStatusYes
keyNoClé recherchée (SIREN, num_finess, code INSEE, …).
messageNoExplication actionnable quand `found=false` (cause probable + remédiation).
Behavior5/5

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

Annotations indicate read-only, non-destructive, idempotent. Description adds context: data freshness parameter behavior, stale data, null email, abbreviation. No contradictions.

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: main purpose, then details, then notes on data quality and limitations. Not overly verbose, but could be slightly more concise.

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 annotations, schema, and output schema exist, description covers return types, special cases (not found), data freshness, and cross-references. No gaps for agent decision-making.

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 has full coverage with descriptions. Description adds meaning for include_freshness (explains effect, cache, cost) and notes num_finess is the exact 9-digit number. Slightly above baseline.

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 specifies the tool retrieves detailed establishment info by FINESS number, enumerating fields like raison sociale, address, GPS. It distinguishes from siblings (e.g., etablissement_by_siret) by its unique identifier.

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 data is 1-2 months stale, recommending cross-checks with ARS for emerging structures. Notes email is always null and raison_sociale is abbreviated, suggesting alternatives for full legal name.

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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