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cturkieh

France Data MCP

rpps_dans_etablissement

Read-onlyIdempotent

List health professionals (RPPS) attached to a FINESS establishment. Distinguishes between liberal and salaried practitioners.

Instructions

Liste les PS rattachés à un établissement FINESS (num_finess 9 chiffres). Pivot RPPS↔FINESS — répond à "qui travaille dans ce labo / hôpital / clinique ?". Le mode_exercice distingue les libéraux exerçant sur place (vacations) des salariés. Couverture : RPPS expose ce lien quand le PS l'a déclaré ; salariés CH/CHU/cliniques bien couverts.

Sortie compacte : coords et distance_km sont null (le tool est par établissement, pas spatial — pour la géoloc, pivoter via etablissement_by_finess sur le num_finess). Catégorie par défaut : Civil (C, ~97 % — libéraux, salariés privés, hospitaliers contractuels). Opt-in : include_agents_publics: true ajoute Agents publics (M, ~0,3 % — PH titulaires, ARS, CNAM, Éducation nationale, PMI, militaires SSA) ; include_etudiants: true ajoute Étudiants (E, ~2,5 % — internes, externes, élèves IDE/SF). Réf : https://mos.esante.gouv.fr/NOS/TRE_R09-CategorieProfessionnelle/. Source : Annuaire Santé, Agence du Numérique en Santé (ANS) — Licence Ouverte v2.0

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
num_finessYes
include_etudiantsNo
include_agents_publicsNo
limitNo
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`.
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_*).
resultsYesEntrées métier (shape spécifique au tool, cf. description du tool).
query_metadataNoMetadata de la query (radius_km, departement, filtres appliqués, …).
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.
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.
Behavior4/5

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

Annotations already declare readOnly, non-destructive, idempotent. The description adds value by explaining coverage (RPPS exposes link when declared, hospital employees well covered), output compression (null coords), and default category (Civil ~97%). 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?

The description is longer than average but each sentence adds value. It is front-loaded with purpose and structured logically: purpose, output details, coverage, opt-ins, source. Minor redundancy but overall effective.

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 the complexity (5 parameters, output schema exists), the description covers all essential aspects: purpose, parameter usage (including opt-ins), output limitations (null spatial fields), and data source/reference. It is complete enough for an AI agent to invoke correctly.

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 20%, so description compensates. It explains num_finess (9 digits), include_etudiants (adds 2.5% category), and include_agents_publics (adds 0.3% category) with percentages and examples. limit and include_freshness are not described in text, but include_freshness has its own schema description. Overall adds significant meaning.

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 lists professionals attached to a FINESS establishment, specifies the exact resource (PS rattachés, num_finess), and distinguishes from sibling tools by being a RPPS↔FINESS pivot. It answers a specific question ('qui travaille dans ce labo/hôpital/clinique ?').

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?

The description explicitly states when to use (find professionals by establishment) and when not to use (for spatial queries, since coords and distance_km are null). It provides alternatives (etablissement_by_finess for geoloc) and explains opt-in parameters and default behavior.

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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