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cturkieh

France Data MCP

inspect_site

Read-onlyIdempotent

Aggregate identification, administrative status, linked professionals, and historical data of a French health establishment in one call. Qualify sites for prospecting or audit.

Instructions

Vue 360 d'un établissement de santé en 1 appel (V0.10). Pendant naturel de panorama_sante_territoire côté site : agrège en parallèle (a) identification FINESS DREES (raison sociale, adresse, téléphone), (b) statut administratif SIRENE via le resolver SIRET (verdicts site + groupe, best_match, SIREN explorés, dinum_errors, explication LLM-friendly), (c) professionnels rattachés via num_finess (sample borné + flag truncated si le site a plus de PS — PAS un count total), (d) historique INSEE (timeline périodes administratives par SIRET candidat).

Remplace 3 appels MCP individuels (verifier_site_actif + rpps_dans_etablissement + historique_etablissement) par 1 seul. Utile pour : prospection (qualifier un site avant outreach), audit territorial (cross-check rapide d'un FINESS suspect), enrichissement CRM en batch.

Format de retour : objet LookupResult. Quand found: true, payload avec 4 sections (finess, statut_site, professionnels, historique). La section historique peut être available: false quand le FINESS existe mais qu'aucun SIRET candidat n'a été identifié (RPPS vide + DINUM 0 match) — dans ce cas le message reprend celui de historique_etablissement. Quand num_finess est absent de FINESS DREES, retourne {found: false, lookupStatus: 'not_found', message}.

Coût : 3 sous-appels parallèles. Cache PostgreSQL absorbe la duplication FINESS-RPC ; le pivot RPPS→DINUM est exécuté en double (verifier + historique partagent la cascade), surcoût p95 ≤ 600 ms — acceptable pour un agrégateur. Pour les besoins ciblés (juste le verdict, juste l'historique), préférer les tools individuels. Payload lourd (~7K tokens) : passer historique_detail: false pour un retour allégé (résumé au lieu des timelines SIRENE complètes) en usage batch.

Alias acceptés : numFiness/finess/idnum_finess.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
num_finessYesNuméro FINESS exact 9 chiffres. Ex: '590048997'.
rpps_limitNoNombre max de PS dans `professionnels.sample`. `professionnels.count` = taille du sample (≤ cette borne), pas le total du site ; `truncated: true` signale qu'il y a davantage de PS. Borné [1, 50]. Défaut 10.
historique_detailNoInclure les timelines SIRENE détaillées dans `historique.siret_timelines` (défaut true). `false` = payload allégé (~7K tokens en moins) : `historique` ne porte qu'un `resume` (counts) + un pointeur vers `historique_etablissement`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
foundYes
lookupStatusYes
keyNoClé recherchée (SIREN, num_finess, code INSEE, …).
messageNoExplication actionnable quand `found=false` (cause probable + remédiation).
Behavior5/5

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

Annotations indicate read-only, non-destructive, idempotent, open-world. The description adds significant behavioral detail: parallel sub-calls, caching, payload size (~7K tokens), behavior when FINESS not found (returns specific structure), and the fact that historique may be unavailable. No contradiction with annotations.

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 sections (purpose, usage, format, cost, aliases). It is relatively long but each part serves a purpose (e.g., cost/payload details are relevant for selection). Could be slightly trimmed, but not excessive.

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 (3 parameters, output schema, multiple sub-calls, caching), the description covers all necessary aspects: return format, error cases, aliases, performance considerations, and usage examples. No gaps identified.

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% with descriptions. The description adds meaning: for 'rpps_limit' it explains that 'professionnels.count' equals the sample size and 'truncated' flags excess; for 'historique_detail' it clarifies payload reduction. This exceeds the baseline of 3 for high 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 'Vue 360 d'un établissement de santé en 1 appel' and explicitly names the three individual MCP calls it replaces. It identifies the tool as an aggregator that combines FINESS, SIRENE, RPPS, and INSEE data into a single call, distinguishing it from sibling tools.

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 lists use cases: prospection, audit territorial, batch CRM enrichment. It also advises when not to use it: 'Pour les besoins ciblés (juste le verdict, juste l'historique), préférer les tools individuels.' This provides clear when-to and when-not-to guidance with references to alternatives.

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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