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france-data-mcp

by io.github.cturkieh

etablissements_finess_in_radius

Read-onlyIdempotent

Recherche d'établissements de santé FINESS dans un rayon géographique (PostGIS ST_DWithin). Filtrable par familles. 24 valeurs disponibles : mco, ssr, sld, had, psychiatrie, dialyse, ambulatoire, labo, imagerie, pharmacie, msp_cpts, ehpad, residence_autonomie, senior_accompagnement, ssiad, aide_domicile, handicap_enfants, handicap_adultes, addictologie, enfance_protection, pmi, hebergement_social, prevention_sante, groupement. Source : FINESS / DREES (dump CSV ingéré localement). 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). Lentille : un filtre familles compte les établissements par leur catégorie FINESS principale. Les activités hébergées dans un site d'une autre catégorie (ex. plateau de biologie d'un hôpital sous famille=labo) ne sont pas comptées — voir le champ perimetre de la réponse. La famille imagerie renvoie le plus souvent 0 résultat (FINESS ne répertorie pas les cabinets d'imagerie).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesLatitude du centre (WGS84).
lonYesLongitude du centre (WGS84).
limitNoNombre max de résultats (1-500, défaut 100).
famillesNoFamilles FINESS à inclure (24 valeurs disponibles, voir enum). Si omis, toutes catégories.
radius_kmNoRayon en km (0.1-50, défaut 5).
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
countYesNombre d'entrées retournées dans `results` (post-troncature).
totalNoEffectif réel avant troncature. Présent sur les tools de nomenclature paginés (lister_*) : `count` = échantillon, `total` = total réel, re-appeler avec un `limit` supérieur si `truncated`.
resultsYesEntrées métier (shape spécifique au tool, cf. description du tool).
freshnessNoFraîcheur des sources (présent si `include_freshness: true`).
perimetreNoLentille de la source : ce que le comptage inclut/exclut. Lire `completeness_note` et la restituer au lecteur final.
truncatedNotrue si le total réel dépasse `limit` (re-paginer via `offset` si supporté, ou augmenter `limit` sur les lister_*). Optional sur les tools de listing exhaustif (lister_*).
query_metadataNoMetadata de la query (radius_km, departement, filtres appliqués, …).
activite_hebergeeNoCompte juxtaposé des sites hébergeant l'activité correspondant à la famille filtrée, sous une autre catégorie FINESS. Distinct du `count` principal — lire `note` pour comprendre la sémantique et ne JAMAIS additionner les deux comptes sans préciser leur nature.

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already signal read-only, idempotent, non-destructive. Description adds crucial caveats: email always null, raison_sociale abbreviation, family filtering limitation (périmètre), imagerie returning 0, and cache behavior. 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.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is comprehensive but somewhat long and not front-loaded. Every sentence adds value, but could be more concise with bullet points or shorter sentences for easier scanning.

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 (not shown) so return value explanation not needed. Description covers all key aspects: purpose, parameters, data source, limitations (email, abbreviation), filter behavior, and performance (cache). Addresses edge cases like imagerie and famille filtering.

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%, so baseline 3. Description adds significant context beyond schema: explanation of families filtering nuance, email null, raison_sociale truncation, include_freshness purpose, and radius limits.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it searches FINESS healthcare establishments within a radius using PostGIS. It lists 24 filterable families and data source. While it doesn't explicitly differentiate from sibling radius tools (e.g., entreprises_in_radius), the entity type (FINESS establishments) is distinct enough.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool vs. alternatives like centres_sante_in_radius or professionnels_in_radius. The description focuses on behavior but omits selection criteria.

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.

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