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france-data-mcp

by io.github.cturkieh

historique_etablissement

Read-onlyIdempotent

Reconstitue la timeline complète d'un établissement de santé (ouvertures, fermetures, changements de NAF/enseigne) en croisant FINESS DREES ↔ resolver SIRET (RPPS + DINUM) ↔ SIRENE INSEE V3.11. Lit les periodesEtablissement complètes pour chaque SIRET candidat.

V0.7.0 : SIRET candidats élargis via le resolver — inclut désormais les SIRET fermés du SIREN parent qui matchent l'adresse FINESS (invisibles côté RPPS seul). Permet de tracer la fermeture exacte d'un site même quand FINESS le liste encore actif.

Usage typique :

  • Tracer l'historique d'un site après une fusion-acquisition

  • Identifier la date de fermeture exacte d'un SIRET encore listé actif côté FINESS

  • Comprendre une cascade de rebrandings via les changements de enseigne1Etablissement au fil des périodes

Format : objet LookupResult. Quand found: true, retourne finess (vue DREES synthétique) + siret_timelines (1 entrée par SIRET candidat avec periodes chronologiques).

Coût : 1 RPC FINESS + 1 SELECT rpps + N appels DINUM + N appels INSEE en parallèle (N ≤ 5 typiquement). Pas de cache.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
num_finessYesNuméro FINESS exact (9 chiffres).

Output Schema

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

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already indicate read-only and idempotent behavior. The description adds detailed behavioral context: it reads multiple sources, mentions the cross-referencing approach, version details (V0.7.0), and cost breakdown (RPC, SELECT, parallel calls). 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 for purpose, version details, usage examples, output format, and cost. While slightly long, it front-loads the key purpose and provides valuable context without unnecessary verbosity.

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 that an output schema exists and the input parameter is fully covered, the description provides complete context: explains the output structure (LookupResult with found flag, finess view, siret_timelines), addresses versioning, and mentions cost and lack of caching. The tool's complexity is well-handled.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the description adds no additional parameter semantics beyond what the schema provides (exact 9-digit FINESS number). The parameter description in the schema is sufficient, so the baseline score of 3 is appropriate.

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 reconstructs the complete timeline of a healthcare establishment by cross-referencing multiple data sources (FINESS, RPPS, DINUM, INSEE). It distinguishes from sibling tools like 'etablissement_by_finess' by focusing on historical reconstruction rather than just current data.

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 three typical use cases (tracing history after merger/acquisition, identifying exact closure dates, understanding rebranding cascades), which helps identify when to use the tool. However, it does not explicitly contrast with alternatives or state when not to use it.

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.

Resources