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cturkieh

France Data MCP

professionnels_rpps_in_radius

Read-onlyIdempotent

Search all types of healthcare professionals (liberal, salaried, hospital, replacements) within a radius using RPPS data. Filter by profession, specialty, and practice mode.

Instructions

Recherche de professionnels de santé dans un rayon via le RPPS (Annuaire Santé ANS). À la différence de professionnels_in_radius (Ameli, libéraux conventionnés uniquement), cette recherche couvre tous les PS : libéraux, salariés (hospitaliers, salariés en cabinet), mixtes, remplaçants. Filtres : profession_codes (nomenclature ANS — ex: 10 Médecin, 60 Infirmier), savoir_faire_codes (spécialité fine DES/DESC), mode_exercice_codes. Codes mode_exercice ANS : L libéral, S salarié, M mixte, R remplaçant, B bénévole, A autre. Par défaut, ne renvoie que les PS de catégorie Civil (C) — droit privé : libéraux, salariés privés, hospitaliers contractuels, ~97 % de la base. Passer include_agents_publics: true pour inclure aussi les Agents publics (M) — fonctionnaires d'État + collectivités + militaires SSA, ~0,3 % (PH titulaires, médecins inspecteurs ARS, médecins conseils CNAM, médecins scolaires Éducation nationale, médecins PMI). Passer include_etudiants: true pour inclure aussi les Étudiants (E) — internes, externes, élèves IDE/SF, ~2,5 %. Source nomenclature : https://mos.esante.gouv.fr/NOS/TRE_R09-CategorieProfessionnelle/. Coords au centroïde commune (~3 km moyenne) — pour précision adresse, croiser num_finess retourné avec etablissement_by_finess. Source : Annuaire Santé, Agence du Numérique en Santé (ANS) — Licence Ouverte v2.0

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
centerYesCentre du cercle de recherche (coordonnées WGS84).
radius_kmYesRayon en km (0.1-50).
profession_codesNoCodes profession ANS (ex: ['10'] Médecin, ['60'] Infirmier). Si omis, toutes professions.
savoir_faire_codesNoCodes savoir-faire ANS (spécialités fines DES/DESC). Si omis, tous savoir-faire.
mode_exercice_codesNoCodes mode d'exercice ANS (libéral / salarié / mixte). Si omis, tous modes.
include_etudiantsNo
include_agents_publicsNo
limitNoNombre max de résultats retournés (défaut serveur 100).
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) 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).
truncatedNotrue si le total réel dépasse `limit` (re-paginer via `offset` si supporté). 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`).
Behavior5/5

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

Annotations already declare `readOnlyHint=true`, `destructiveHint=false`, `idempotentHint=true`, and `openWorldHint=true`. The description adds substantial behavioral context: coordinates are commune centroids (~3km average), suggests cross-referencing `num_finess` for precision, mentions server-side cache (5 min, negligible cost), and explains source and license. 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 long but well-structured: first sentence states purpose and sibling comparison, then parameter details, then default behavior, coordinate precision, and source. Every sentence adds value, though it could be slightly more concise without losing information.

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 tool's complexity (9 parameters, nested objects), the description is comprehensive. It covers source, license, default filters, coordinate precision, caching, and the option to include additional categories. The presence of an output schema means return values don't need elaboration. No major gaps.

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 78%, but the description compensates by explaining parameter semantics for many: `profession_codes`, `savoir_faire_codes`, `mode_exercice_codes` with examples and links to nomenclature. For `include_agents_publics` and `include_etudiants`, it provides detailed context (percentages, examples of professionals). For `include_freshness`, it explains the opt-in behavior and caching. The description adds meaning beyond the 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 searches for health professionals within a radius using RPPS (Annuaire Santé ANS). It specifies 'tous les PS' covering all types, and explicitly contrasts with the sibling `professionnels_in_radius` which only covers liberal conventioned professionals. This provides a specific verb+resource with clear differentiation.

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

Usage Guidelines4/5

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

The description contrasts this tool with `professionnels_in_radius`, guiding when to use each. It explains default filtering (only Civil category) and how to include other categories via `include_agents_publics` and `include_etudiants`. However, it lacks an explicit 'use this when' vs 'do not use when' statement, though the comparison is strong.

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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