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inspect_site

Read-onlyIdempotent

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

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, destructiveHint=false, and idempotentHint. The description adds valuable context: it's an aggregator with parallel sub-calls, returns LookupResult with sections, handles truncation for professionals (not count), and describes edge cases (missing FINESS, unavailable historique). No contradictions 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 relatively long but well-structured with sections and bullet points. It includes examples and explicit guidance. While not extremely concise, every sentence adds value.

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 (aggregator with multiple sub-calls, optional parameters, edge cases) and the presence of an output schema, the description fully covers return format, failure modes, performance considerations, and aliases. It leaves no critical gaps.

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%, so baseline is 3. The description adds significant value: explains that rpps_limit is a sample (not total count) with truncated flag, that historique_detail reduces payload by ~7K tokens, and mentions aliases for num_finess. This enhances understanding 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 explicitly states it provides a '360 view of a health establishment in 1 call' and aggregates four types of data (identification, administrative status, professionals, history). It distinguishes from siblings by noting it replaces three individual tools (verifier_site_actif, rpps_dans_etablissement, historique_etablissement) with a single call.

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 clearly states when to use (prospection, audit territorial, CRM enrichment) and when not (for specific needs like just verdict or history, prefer individual tools). It also provides guidance on payload weight and suggests setting historique_detail=false for batch usage.

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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TDQS

A4/5.0
Disambiguation3/5

While tools have distinct purposes, there is overlap among several similar tools (e.g., multiple professional and establishment search tools). The detailed descriptions help differentiate, but an agent may struggle to choose correctly among them.

Naming Consistency2/5

Naming mixes French and English, with no consistent pattern (e.g., 'enrichir_concurrents' vs 'inspect_site', 'etablissement_by_finess' vs 'etablissements_finess_in_radius'). This inconsistency makes the toolset harder to navigate.

Tool Count3/5

36 tools is high but justified given the broad domain. However, there are multiple tools for similar tasks (e.g., four professional search tools), suggesting some redundancy. The scope is borderline but acceptable.

Completeness4/5

The toolset covers a wide range of needs for French health data analysis: establishments, professionals, population, geocoding, demographics, and composite analyses. Few obvious gaps exist, though some specialized tasks might require additional integration.