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cturkieh

France Data MCP

panorama_implantation_complet

Read-onlyIdempotent

Aggregates 7 data sections (territory, demand, competitors, prescribers, etc.) in a single call for lab implantation studies. Returns summaries only, not raw lists.

Instructions

Étude d'implantation labo en 1 appel (V0.23). Géocode l'adresse cible puis agrège EN PARALLÈLE 7 sections : territoire (densités PS commune vs national + établissements), demande (profil démographique du BASSIN — rayon — via profil_iris : âge, CSP, revenu pondéré), concurrents (labos FINESS), pourvoyeurs (MCO/EHPAD/SSR/dialyse — drivers écosystémiques), prescripteurs (médecins RPPS + IDEL Ameli), cds (centres de santé), referentiels (qualité couverture FINESS↔SIRENE).

Remplace ~15 appels MCP individuels par 1. Renvoie des RÉSUMÉS (count / top-N / moyenne), JAMAIS de listes brutes. AUCUNE interprétation métier (pas de 'désert médical' ni de verdict GO/NO-GO) — le caller LLM applique sa grille.

DÉGRADATION (lis couverture — 1 drapeau par section) : "ok" | "partiel:<raison>" | "indisponible:<raison>". Si une source est down, SA section est flaggée et le RESTE est renvoyé — comble alors le trou via l'outil unitaire correspondant (etablissements_finess_in_radius, professionnels_rpps_in_radius, densite_sante, centres_sante_in_radius…). Échec d'ANCRAGE (géocodage KO / adresse douteuse / code INSEE indérivable) = rejet total (RangeError).

Pièges internalisés : Paris/Lyon/Marseille basculés sur le département (meta.plm_mode=true) ; prescripteurs expose precis_count (PS géolocalisés à l'adresse, pas au centroïde commune) ; cds sans distance individuelle (centroïde commune).

WORKFLOW : appelle CET outil pour DÉMARRER une étude, puis creuse les sections partiel/indisponible via les unitaires, puis enrichir_concurrents sur le top 3 de concurrents.top.

Sources : IGN (géocodage), FINESS DREES, RPPS/ANS, Ameli/CNAM, INSEE/FILOSOFI, SIRENE/DINUM.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
adresseNoAdresse cible, géocodée en interne via IGN. Ex: "12 rue Nationale, Lille". XOR avec `point`.
pointNoCoordonnées { lat, lon } si déjà connues (skip géocodage). Fournir `code_insee` avec.
code_inseeNoCode INSEE commune (avec `point`, quand le géocodage est déjà fait).
rayon_kmNoRayon du bassin de l'étude (km). Défaut 5.
Behavior5/5

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

Annotations provide basic traits; description adds rich behavioral details: parallel execution, degradation flags, geocoding failure throws RangeError, special handling for big cities, and precision notes for prescripteurs and cds.

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?

Structured with clear sections: overview, replacement of calls, degradation, pitfalls, workflow. Slightly verbose in places but well-organized and front-loaded with core purpose.

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 no output schema, description comprehensively explains output format (summaries only), each section's content, failure modes, and follow-up steps. Lists sibling tools for fallback. Highly complete.

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?

Input schema covers all 4 parameters with descriptions (100% coverage). Description adds context: XOR constraint between adresse and point, need for code_insee with point, and default rayon_km. Adds value beyond 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 it performs a lab implantation study in one call, aggregating 7 sections in parallel. It distinguishes itself from siblings by replacing ~15 individual MCP calls.

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 instructs to call this tool to start a study, then use unitaries for partial/indisponible sections, then enrich top 3 competitors. Also warns about degradation and no business interpretation.

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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