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Pillr

Prix au m² d'une commune

prix_m2_commune
Read-onlyIdempotent

Prix de référence au mètre carré d'une commune française, appartement et maison, tel que Pillr le publie : valeur, provenance (mesurée sur les ventes DVF ou estimée), millésime et date de reconstruction. Un prix absent est dit absent, jamais deviné. C'est un repère de marché communal, pas l'estimation d'un bien précis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
communeYesNom de la commune (« Bordeaux », « Lyon 7e », « Saint-Étienne ») ou code INSEE à 5 chiffres. Un code postal est accepté en dernier recours.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

The description adds substantial behavioral detail beyond the read-only and idempotent annotations: provenance is either measured from DVF sales or estimated, the result includes millésime and reconstruction date, and absent prices are reported as absent rather than guessed. This strongly informs correct interpretation of results.

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?

The description is compact and information-dense: every sentence adds a distinct fact, and the core resource is front-loaded before provenance, absence semantics, and boundary notes. There is no fluff or 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?

For a single-parameter read-only tool with no output schema, the description is complete enough. It lists the returned data elements, explains provenance and absence behavior, and clarifies what the result is not, leaving little ambiguity for correct invocation and interpretation.

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?

The input schema already documents the single 'commune' parameter and its accepted forms with 100% schema_description_coverage, so the description does not need to compensate. The description only adds general scope and does not repeat or extend the parameter formatting rules.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool's resource and scope: a reference price per square meter for a French commune, covering apartment and house values, provenance, millésime, and reconstruction date. It also explicitly rules out per-property valuation, but it does not explicitly differentiate against the sibling tools 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 states a clear boundary: this is a communal market benchmark, not an estimate for a specific property, so an agent should not use it for property-level valuation. It gives enough context to infer when it is appropriate, though it does not mention sibling tools or alternative routes explicitly.

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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