Skip to main content
Glama

france-data-mcp

by io.github.cturkieh

professionnel_by_rpps

Read-onlyIdempotent

Récupère la fiche complète d'un PS par identifiant national (rpps_id / IDNPS, 11 ou 12 chiffres — IDs émis depuis 2020 ont un préfixe "81" = 12 chars ; anciens IDs = 11 chars). Renvoie N entrées quand le PS exerce sur plusieurs sites (1 par site, chacun avec sa propre geo_precision — un même PS peut donc cumuler un site précis FINESS et un site au centroïde commune).

Chaque résultat géolocalisé porte geo_precision ∈ {"adresse", "etablissement_finess", "centroide_commune"} — lire ce champ pour évaluer la fiabilité des coords (précise BAN/FINESS au m près vs centroïde commune ~3 km, non discriminant intra-commune).

Fallback automatique sur l'API FHIR ANS live (gateway.api.esante.gouv.fr/fhir/v2) si non trouvé en base locale (snapshot mensuel J-30 max). Le champ source distingue db (base locale) de ans_fhir (live). include_freshness n'affecte que source: "db". Source : Annuaire Santé, Agence du Numérique en Santé (ANS) — Licence Ouverte v2.0

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rpps_idYes
include_freshnessNoSi true, ajoute un champ `data_freshness` au payload (dans `query_metadata` si présent, sinon à la racine) listant la dernière ingestion réussie par source (FINESS, Ameli, RPPS, CDS) avec `staleness_days`. Opt-in pour ne pas alourdir les payloads par défaut. Cache 5min côté serveur — coût négligeable.

Output Schema

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

TDQS

A4.5/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: fallback to ANS FHIR API, geo_precision semantics, source field differentiation, cache behavior, and data freshness details. No contradiction with readOnlyHint (true) or other annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three paragraphs and somewhat verbose, but each sentence provides necessary information. It is front-loaded with the main purpose, though some technical details (e.g., geo_precision values) could be more succinct.

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 effectively covers key behavioral details: multi-site results, fallback, source distinction, freshness options. It is complete for an ID-based retrieval tool with moderate complexity.

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?

The input schema covers 2 parameters with 50% description coverage. The description adds meaning by detailing the rpps_id format (11-12 digits, prefix pattern) and explaining that include_freshness only affects 'db' source. This compensates for the missing schema description on rpps_id.

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 a professional's full record by national identifier, specifies the identifier format (11-12 digits, '81' prefix), and notes it returns multiple entries for multi-site practices. This distinguishes it from sibling tools like rpps_search_by_name.

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 implies usage when a specific rpps_id is known, but does not explicitly state when not to use it or mention alternative tools for search or radius queries. The context is clear enough for an agent to infer appropriate use.

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