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cturkieh

France Data MCP

compare_raison_sociale_finess_vs_rpps

Read-onlyIdempotent

Compare corporate names from FINESS DREES and RPPS/Annuaire Santé for a given FINESS number to identify factual divergences, such as recent post-M&A rebranding.

Instructions

Compare la raison sociale FINESS DREES vs RPPS / Annuaire Santé ANS pour un même num_finess. Primitive brute SANS interprétation métier — retourne juste les deux libellés + un statut de comparaison. Le caller décide quoi faire de la divergence.

Utilité : RPPS reflète souvent plus rapidement les rebrandings post-M&A que FINESS DREES (ex: un site racheté reste 'DIAGNOVIE' chez DREES alors qu'il est déjà 'BIOGROUP NORD' chez l'ANS). Ce tool expose la divergence factuelle ; il NE DIT PAS qui a racheté qui (ça repose sur de la connaissance d'enseignes commerciales non publique).

Statut renvoyé (champ statut présent uniquement sur la branche found: true) :

  • exact_match : FINESS et ≥1 RPPS sont strictement égaux après normalisation

  • divergent_after_normalization : aucune RPPS ne matche FINESS — vraie divergence

  • rpps_absent : aucune RPPS n'a déclaré ce FINESS (pivot impossible)

Format : objet LookupResult discriminé par found. Quand num_finess est absent de FINESS DREES, le tool retourne {found: false, lookupStatus: 'not_found', message, ...} — il n'y a PAS de champ statut dans ce cas.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
num_finessYesNuméro FINESS exact (9 chiffres).

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?

The description discloses the output structure (LookupResult, found field, statut values) and edge cases (missing num_finess returns found: false without statut). Annotations already indicate read-only, idempotent, open-world; the description adds detailed behavioral context beyond annotations 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 comprehensive but slightly lengthy; it includes a use case and status explanation. Every sentence adds value, and the main purpose is front-loaded. Could be shortened but remains effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (comparison of two data sources), the description covers the output structure, statuses, and not-found scenario. Although an output schema exists (not shown here), the description adequately explains the return values. A slight gap is not naming the field for the two labels, but overall complete.

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?

Schema coverage is 100% with a clear description for num_finess (exact 9 digits). The description repeats the format but adds no new semantic information beyond the schema. This meets the baseline of 3.

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 compares raison sociale from FINESS DREES vs RPPS for a given num_finess, emphasizing it is a raw primitive without business interpretation. It is distinct from sibling tools like reconcilier_finess_sirene or compare_adresse_cnam_vs_finess.

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 explains when to use it (e.g., to detect post-M&A rebranding mismatches) and explicitly states what it does not do (e.g., say who acquired whom). However, it does not explicitly mention alternatives or when not to use it, but the context is clear.

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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