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france-data-mcp

by io.github.cturkieh

entreprise_by_siren

Read-onlyIdempotent

Récupère le détail d'une entreprise française par son SIREN (9 chiffres) : raison sociale, NAF, finances historiques, dirigeants, établissements. Source : DINUM Recherche Entreprises.

Format de retour : objet LookupResult discriminé par found.

  • found: true → l'entreprise est retournée à plat (champs siren, nomComplet, etablissements, enrichmentStatus, …)

  • found: false → { found: false, key, lookupStatus: 'not_found' | 'ambiguous', message }. not_found : SIREN non indexé par DINUM (souvent diffusion partielle INSEE — l'entreprise peut quand même exister dans SIRENE). ambiguous : régression API à signaler.

⚠️ Quand found: true, la liste etablissements peut être tronquée. Le champ nombreEtablissements (compté SIRENE) reflète le total réel. Lire enrichmentStatus pour savoir si la liste est complète :

  • success : etablissements contient tous les sites

  • partial : sites manquants (multi-département ou NAF différent du siège) — voir enrichmentWarning

  • failed : l'enrichissement a échoué (rate limit, panne API) — seul le siège est listé

  • not_attempted : entreprise monosite ou data SIRENE manquante

Pour énumération exhaustive multi-département, utiliser entreprises_in_radius par zone géographique. Coût : 1 ou 2 appels API DINUM par invocation (rate limit ~1 req/s effectif).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sirenYesSIREN 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 already provide readOnlyHint, openWorldHint, etc. The description adds significant behavioral context: return type (LookupResult discriminated), possible etablissements truncation, meaning of enrichmentStatus values, and that 1-2 API calls are made. 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, output format, and warnings. It is somewhat long but every sentence adds value. Could be slightly more concise, but front-loads key info.

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 output schema exists, the description fully explains the discriminated union (found: true/false), all relevant fields (nombreEtablissements, enrichmentStatus, etc.), and edge cases (not_found, ambiguous, truncation). Comprehensive coverage.

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 describes the single parameter 'siren' as 'SIREN exact, 9 chiffres.' with 100% coverage. The description repeats 'SIREN (9 chiffres)' but adds no extra meaning beyond the schema. Baseline 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 retrieves French company details by SIREN, listing key fields (raison sociale, NAF, finances, dirigeants, établissements) and the data source (DINUM). It distinguishes itself from siblings like 'etablissement_by_siret' (by SIRET) and 'entreprises_in_radius' (exhaustive enumeration).

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 advises when to use alternatives: 'Pour énumération exhaustive multi-département, utiliser `entreprises_in_radius`'. It also mentions the rate limit (~1 req/s) and recommends reading 'enrichmentStatus' for completeness.

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.

Resources