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cturkieh

France Data MCP

data_freshness

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Check the staleness of ingested French health data sources (FINESS, Ameli, RPPS, Centres de Santé) to verify data is current before audits or temporal analysis.

Instructions

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
Behavior5/5

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

Discloses cache duration (5 minutes) and cost (1 SELECT). Annotations indicate readOnlyHint=true, which aligns with description. 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.

Conciseness5/5

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

Description is front-loaded with purpose and structured into logical sections: sources, fields, usage, alerts, exclusions. Each sentence adds information without redundancy.

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?

Comprehensive coverage of all aspects: returns, usage, cache, cost, live source exclusion. Output schema exists, so description doesn't need to detail every field, but it sufficiently explains the tool's role.

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?

Input schema has no parameters, so description adds value by confirming no input needed and detailing output fields. Baseline 4 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?

Description clearly states the tool returns freshness of specific data dumps (FINESS DREES, etc.) with fields and cadences. Distinguishes from sibling tools by focusing on data freshness meta-information.

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?

Provides explicit usage scenario (before territorial audit or temporal analysis) and specific alert thresholds for each source (e.g., staleness_days > 90 for FINESS). Also clarifies that live sources are excluded.

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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