Skip to main content
Glama

france-data-mcp

by io.github.cturkieh

compare_raison_sociale_finess_vs_rpps

Read-onlyIdempotent

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

TDQS

A4.6/5.0
Behavior5/5

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

Annotations set readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds detailed behavioral context: it returns two labels and a comparison status, describes the three possible statut values ('exact_match', 'divergent_after_normalization', 'rpps_absent'), and explains the not_found case. No contradictions.

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 a clear introductory sentence, bullet-pointed statut explanations, and no redundant text. It could be slightly more concise, but each 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 presence of an output schema, the description still covers all essential aspects: use case, return format, edge cases (not found), and statut details. It is complete for a simple tool with one parameter.

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?

The input schema has 100% coverage with a clear description for num_finess ('exact 9-digit number'). The description does not add new parameter information beyond that, but schema coverage is high, so 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 compares 'raison sociale' between FINESS DREES and RPPS/Annuaire Santé ANS for a given num_finess. It uses a specific verb ('compare') and resource, and distinguishes itself from sibling tools like compare_adresse_cnam_vs_finess by focusing on name comparison.

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 states it is a 'primitive brute SANS interprétation métier' and explains when to use it (e.g., to detect rebranding after M&A). It also clarifies what it does not do (does not indicate acquisitions) and provides alternative context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All france-data-mcp toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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