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Glama

Rent indicators (Carte des loyers)

rent_estimate

Retrieve official modelled asking rents for any French commune, segmented by apartments, houses, and room counts. Input a surface area to get an estimated monthly rent.

Instructions

Official modelled asking rents (€/m²/month, charges included) for any French commune: apartments overall, 1-2 rooms, 3+ rooms, and houses. Source: Carte des loyers, Ministère du Logement / ANIL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationYesAddress, commune name or INSEE code
surface_m2NoIf given, also returns the estimated monthly rent

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It discloses that values are modelled, official, charges included, and sourced from the Carte des loyers. It does not mention coverage caveats or address-to-commune resolution, but 'modelled' appropriately signals that these are estimates.

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?

One compact sentence conveys the output, units, geographic scope, property breakdown, and data source with no filler. The key information is front-loaded and every clause earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter lookup with full schema coverage, the description is largely self-sufficient: it tells the agent what is returned and from where. It could be slightly more explicit about how an address maps to a commune and what happens when surface_m2 is omitted, but these are mostly inferable or handled by the schema.

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%, so the baseline is 3. The description adds little parameter-specific meaning beyond the schema: it reinforces that location is commune-oriented, but it does not elaborate on surface_m2 or its interaction with the returned values. The schema already documents both parameters adequately.

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?

States a specific resource ('asking rents') and scope ('any French commune'), plus the exact breakdown by dwelling type. This clearly differentiates it from sibling tools like price_per_m2 and property_sales, which concern sale prices rather than rents.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the use case: obtain official commune-level modelled asking rents. However, it never explicitly contrasts this tool with alternatives such as price_per_m2, estimate_property, or rent_control, and it gives no 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.