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france-data-mcp

by io.github.cturkieh

dynamique_immobiliere

Read-onlyIdempotent

Dynamique immobilière et potentiel de croissance d'une zone (point + rayon). Combine 3 sources officielles : permis de construire (Sit@del/SDES, maille COMMUNE — logements autorisés/commencés récents → habitants attendus), zones AU du PLU (Géoportail de l'Urbanisme/IGN — futurs quartiers réservés, géolocalisés), ventes de terrains à bâtir (DGFiP DVF, géolocalisées). Sortie en 2 registres : 'note' = VOLUME (logements autorisés/commencés, nombre et immédiateté des zones AU) destiné au scoring de potentiel ; 'info' = quartiers concernés (nommés), habitants attendus, prix indicatifs (contexte, hors score). En ville dense les permis-commune sont grossiers → s'appuyer sur zones AU + terrains (géolocalisés). Point côtier/isolé sans commune au géocodage inverse → couverture.permis='indisponible:commune_introuvable' et meta.code_commune=null, MAIS zones AU + terrains restent servis (calcul par rayon) — l'outil ne plante jamais pour ça. 'geojson' = polygones des zones AU pour la carte. Sources : SDES, IGN/GPU, DGFiP.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesLatitude du centre (WGS84).
lonYesLongitude du centre (WGS84).
rayon_kmNoRayon en km (0.1-10, défaut 3).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
infoNoContexte non-scorable : habitants_attendus, quartiers_au (libellés), prix_m2_median, terrains. Ne PAS intégrer à une note d'attractivité.
noteNoDonnées de VOLUME — à utiliser pour le scoring LLM. logements_autorises_recent, logements_commences_recent, zones_au_nombre, zones_au_immediates, signal.
geojsonNoFeatureCollection GeoJSON des polygones des zones AU (pour la carte).
couvertureYesStatut de dégradation par section : 'ok' | 'indisponible:<raison>'. Lire avant d'interpréter note/info.

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already indicate readOnly, idempotent, non-destructive. The description adds significant behavioral context: the tool never fails for missing commune data, it combines multiple sources, and it mentions coarse permit data in dense areas. This goes beyond annotation information.

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 reasonably concise for its complexity, front-loaded with the main purpose. It could be slightly more structured, but it effectively communicates the tool's functionality without unnecessary verbosity.

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 tool's complexity (multiple sources, edge cases, two output registers) and the presence of an output schema, the description is very complete. It covers the overall approach, edge cases, and output contents, leaving no major gaps.

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 coverage is 100% with clear descriptions for all three parameters. The description adds minimal extra detail (e.g., radius used for 'calcul par rayon'), meeting the baseline for high schema coverage.

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's purpose: assessing real estate dynamics and growth potential of a zone. It specifies the three official sources combined and the two output registers ('note' and 'info'), distinguishing it from siblings which likely focus on single sources.

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 provides situational guidance, such as relying on AU zones and land sales in dense cities due to coarse building permit data. It also explains behavior when the commune is not found. However, it lacks explicit 'when not to use' instructions or comparisons to sibling tools.

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