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cturkieh

France Data MCP

by cturkieh
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

densite_professionnels_sante

Read-onlyIdempotent

Compute the density of health professionals per 100,000 inhabitants in a French department, with options to filter by profession, specialty, and mode of exercise, and compare to the national average.

Instructions

Densité de professionnels de santé pour 100 000 habitants dans un département. Méthodo DREES par défaut : médecins (profession_code='10') en activité régulière (libéral + salarié + mixte, codes mode_exercice L, S, M), hors étudiants. Croise RPPS (count) et INSEE Melodi (population municipale PMUN, recensement 2023).

Usages : densité de cardiologues / dermatologues / infirmiers libéraux / pharmaciens / sages-femmes par dept. Pour une spécialité médicale, passer savoir_faire_code (ex SM02 cardiologie). Pour une autre profession que médecin, passer profession_code (60 infirmier, 21 pharmacien, etc.). Pour libéraux seuls, passer mode_exercice_codes: ['1'].

compare_national: true ajoute la densité France entière (DOM inclus) et l'écart en % (positif = sur-doté vs France, négatif = sous-doté). Coût : 1 RPC count_rpps supplémentaire + 1 appel Melodi (cacheable).

Ne renvoie AUCUNE interprétation métier (pas de seuil "désert médical" automatique). Le caller applique sa grille.

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/.

Source : Annuaire Santé, Agence du Numérique en Santé (ANS) — Licence Ouverte v2.0

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
code_deptYesCode INSEE du département (2-3 caractères, ex: '75', '13', '2A', '971').
profession_codeNoCode profession ANS (TRE_R94). Default '10' (Médecin). Ex : '60' Infirmier, '21' Pharmacien, '50' Sage-femme, '40' Chirurgien-dentiste, '70' Masseur-kinésithérapeute.
savoir_faire_codeNoCode spécialité (savoir_faire). Pertinent surtout pour profession_code=10 (médecin). Ex : 'SM02' Cardiologie, 'SM26' Dermato-vénérologie. Voir lister_specialites_medicales (V0.8 Phase 4).
mode_exercice_codesNoCodes mode_exercice ANS à inclure. Default ['L','S','M'] (libéral + salarié + mixte = activité régulière DREES). Passer ['L'] pour libéraux seuls. Codes mode_exercice ANS : L libéral, S salarié, M mixte, R remplaçant, B bénévole, A autre.
compare_nationalNoAjoute le calcul France entière + écart relatif en % (recommandé pour qualifier 'sous-doté'/'sur-doté').
include_etudiantsNo
include_agents_publicsNo

Tool Definition Quality

A4.7/5.0
Behavior5/5

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

The description details data sources (RPPS, INSEE Melodi), default category (Civil C), and options to include public agents and students with percentages. It mentions caching for compare_national. Annotations indicate readOnlyHint, idempotentHint, openWorldHint, and the description adds context beyond these without contradiction.

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 well-structured with paragraphs and bullet points, front-loading the main purpose and methodology. It is verbose but covers necessary details; minor trimming could improve conciseness without losing clarity.

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 7 parameters (only 1 required) and no output schema, the description thoroughly explains input options, default behaviors, and output (density, optional national comparison). It addresses edge cases (public agents, students) and data sources, making it complete for agent understanding.

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?

The description adds significant meaning to parameters: explains defaults (profession_code='10', mode_exercice_codes=['L','S','M']), provides examples (savoir_faire_code='SM02'), and clarifies effects of booleans (compare_national, include_etudiants, include_agents_publics). This enriches the schema, which has 71% coverage.

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 computes density of health professionals per 100,000 inhabitants in a department, specifying methodology (DREES). It distinguishes from siblings like densite_etablissements_sante by focusing on professionals rather than establishments.

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 provides explicit usage scenarios (cardiologists, nurses, etc.) and explains how to filter by profession_code, savoir_faire_code, and mode_exercice_codes. It also describes the compare_national option. However, it does not explicitly state when not to use this tool or give direct alternatives for other density queries.

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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