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france-data-mcp

by io.github.cturkieh

data_freshness

Read-only

Retourne la fraîcheur des dumps de données ingérés côté serveur : FINESS DREES (bimestriel), Annuaire Santé Ameli (hebdomadaire), RPPS / Annuaire Santé ANS (mensuel), Centres de Santé CNAM (hebdomadaire). Pour chaque source : last_success_at ISO timestamp, last_success_row_count, last_attempt_at, last_attempt_status, staleness_days (jours depuis la dernière ingestion réussie), cadence_hint (cadence attendue côté éditeur).

Usage typique : avant un audit territorial ou une analyse temporelle, le caller appelle ce tool pour savoir si les données sont à jour. Une staleness_days > 90 côté FINESS = alerte (dernier sync DREES manqué), > 14 côté Ameli = alerte (job hebdo cassé), > 45 côté RPPS = alerte (job mensuel cassé), > 14 côté CDS = alerte (job hebdo cassé).

Les sources LIVE (DINUM Recherche Entreprises, INSEE SIRENE V3.11, ANS FHIR live) ne sont PAS listées ici puisqu'elles n'ont pas de cycle d'ingestion — leur fraîcheur est celle des API amont (live, ~secondes).

Cache serveur : 5 minutes. Coût : 1 SELECT sur ingest_log au pire (sinon hit cache).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourcesYes

TDQS

A4.8/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true and idempotentHint=false. The description adds behavioral details: a 5-minute server cache, a single SELECT cost at worst on ingest_log, and the fact that live sources are not listed. This informs the agent about performance and data freshness behavior beyond 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, front-loading the main purpose and listing details in subsequent sentences. It is fairly long but every sentence adds value. Minor redundancy could be trimmed, but overall clear and concise.

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 no input parameters and an existing output schema, the description thoroughly explains all aspects: the sources, returned fields, usage context, alert thresholds, excluded sources, and caching behavior. It is complete and leaves no gaps.

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?

With 0 parameters and schema coverage 100%, baseline is 4 per guidelines. The description explains the output fields and staleness thresholds, adding meaningful context for interpreting results, though no parameters are present.

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 identifies the tool's purpose: returning data freshness for specific ingested sources (FINESS, Ameli, RPPS, Centres de Santé). It lists the exact sources and the fields returned, and distinguishes from sibling tools that perform specific queries or analyses.

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 typical usage before territorial audits or time analyses. It provides concrete alert thresholds for staleness (e.g., >90 days for FINESS) and names which sources are excluded (live APIs). This gives clear when-to-use and when-not-to-use guidance.

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

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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