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france-data-mcp

by io.github.cturkieh

entreprises_in_radius

Read-onlyIdempotent

Recherche d'entreprises françaises avec filtres NAF, code postal, département ou rayon géographique. Couvre tous secteurs (santé via NAF 8690B, 4773Z, 8710A, 8621Z, etc.). Source : DINUM Recherche Entreprises (SIRENE + RNE). Renvoie CA, dirigeants, tranches d'effectif et dates de création.

Deux modes EXCLUSIFs (endpoints DINUM distincts) : (1) proximité — lat+lon+radiusKm (optionnellement + naf), résolu nativement via /near_point ; (2) administratif — q (texte libre) et/ou naf + codePostal/departement, via /search. La recherche de proximité ne supporte PAS q ni codePostal/departement (combinaison rejetée avec une erreur explicite : choisir un seul mode). radiusKm borné à 50 km.

Réduction de payload (V0.13) : includeDirigeants: false strip la liste des dirigeants RNE de chaque entreprise du résultat — utile en énumération volume (Geo Intel) où les dirigeants ne sont pas exploités et où les groupes type Biogroup peuvent en lister 20+ par entité (gonflement inutile du payload). Défaut true pour préserver le contrat V0.12 (backward-compat strict).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoRecherche textuelle libre (raison sociale, dirigeant…).
latNoLatitude du centre du cercle de recherche.
lonNoLongitude du centre du cercle de recherche.
nafNoCode NAF principal (ex: '8690B' = labos, '4773Z' = pharmacies, '8710A' = EHPAD, '8621Z' = MG).
pageNoPage (1-indexed).
perPageNoRésultats par page (1-25, défaut 10).
radiusKmNoRayon en km (1-50).
codePostalNoFiltre alternatif : code postal exact.
departementNoFiltre alternatif : code département.
includeDirigeantsNoInclure la liste des dirigeants RNE dans chaque entreprise (défaut true). `false` strip `dirigeants: []` côté handler — utile en énumération volume où les dirigeants ne sont pas exploités (économie de tokens, groupes type Biogroup peuvent lister 20+ dirigeants par entité).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageYes
totalYesTotal d'entreprises matchant la query côté DINUM.
perPageYes
totalPagesYes
entreprisesYesEntreprises retournées (SIREN, nomComplet, NAF, finances, etablissements).

TDQS

A4.8/5.0
Behavior5/5

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

Annotations (readOnlyHint=true, etc.) are already provided, and the description adds significant context: source (DINUM Recherche Entreprises), what is returned (CA, dirigeants, etc.), the behavior of the two modes, the constraint that proximity mode does not support administrative filters, and the effect of includeDirigeants. 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 into three paragraphs: general purpose, mode details, and parameter note. It is front-loaded with the main action. While it is fairly long, every sentence adds value and no information is redundant. Slightly more conciseness could be achieved, but it is acceptable.

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 complexity (10 parameters, two modes, output schema exists), the description covers all necessary aspects: mode exclusivity, parameter combinations, limits (radius 50 km), a specific performance optimization (includeDirigeants), and the data sources. An agent can correctly select and invoke this tool without ambiguity.

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 inline descriptions, so baseline is 3. The description adds value beyond the schema by explaining which parameters belong to which mode, that q and codePostal/departement are exclusive with lat/lon/radiusKm, and the practical use of includeDirigeants for payload reduction. This extra context raises the score.

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 it searches French companies with filters (NAF, postal code, department, geographic radius), specifies the two exclusive modes (proximity and administrative), and lists the data returned (CA, dirigeants, etc.). It distinguishes from siblings like entreprise_by_siren or etablissements_in_radius by focusing on companies and the dual-mode search.

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 details two exclusive modes: proximity (lat+lon+radiusKm, optionally naf) and administrative (q and/or naf+codePostal/departement). It states that mixing modes is rejected with an error, provides radius limit (50 km), and explains the payload reduction option (includeDirigeants). This gives clear when-to-use guidance.

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
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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.

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