Skip to main content
Glama
cturkieh

France Data MCP

entreprises_in_radius

Read-onlyIdempotent

Find French companies within a geographic radius using NAF codes, postal code, or department. Search by activity and location to get company details like revenue and directors.

Instructions

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.

Limitation API DINUM : la combinaison naf + lat/lon/radiusKm n'est pas supportée nativement (lat/lon nécessitent un q textuel). Le serveur applique alors un fallback : reverseGeocode du point → recherche par département → filtrage Haversine côté serveur. Les résultats sont limités aux 25 premières entreprises du NAF dans le département (limite API).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nafNoCode NAF principal (ex: '8690B' = labos, '4773Z' = pharmacies, '8710A' = EHPAD, '8621Z' = MG).
qNoRecherche textuelle libre (raison sociale, dirigeant…).
lonNoLongitude du centre du cercle de recherche.
latNoLatitude du centre du cercle de recherche.
radiusKmNoRayon en km (1-50).
codePostalNoFiltre alternatif : code postal exact.
departementNoFiltre alternatif : code département.
perPageNoRésultats par page (1-25, défaut 10).
pageNoPage (1-indexed).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalYesTotal d'entreprises matchant la query côté DINUM.
pageYes
perPageYes
totalPagesYes
entreprisesYesEntreprises retournées (SIREN, nomComplet, NAF, finances, etablissements).
fallbackNoPrésent uniquement si le serveur a appliqué le fallback reverseGeocode + Haversine.
truncated_by_per_pageNotrue si le post-filtre Haversine a tronqué pour respecter `perPage`.
Behavior4/5

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

Annotations already indicate read-only and idempotent behavior. The description adds valuable transparency about the API limitation and fallback mechanism when using specific parameter combinations, and the result limit. 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?

Description is two paragraphs, first clearly explaining purpose and capabilities, second covering limitations. It is front-loaded with key information and each sentence serves a purpose. Could be slightly more concise but overall efficient.

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 9 parameters fully described in schema and the existence of an output schema, the description covers the main behavior and important edge cases (fallback, limits). It does not repeat pagination details but perPage and page are in schema. Adequate for a search tool with complex filtering.

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?

All 9 parameters have schema descriptions (100% coverage). The description adds context on how naf interacts with lat/lon/radius and explains the fallback, which is not evident from the schema alone. This enhances understanding of parameter combinations.

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 for French companies with multiple filter options (NAF, postal code, department, geographic radius) and lists the data returned (CA, dirigeants, effectifs, dates). It distinguishes from siblings by specifying 'entreprises' and covering all sectors with examples.

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 this tool (searching companies with various filters) and includes important usage limitations (fallback behavior when combining naf with lat/lon/radius, 25-result limit). It does not explicitly mention alternatives like professional-specific tools, but the context from sibling names suggests that.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/cturkieh/france-data-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server