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Glama

Real property sales around an address (DVF)

property_sales

Find notarized property sales in France using official DVF data. Get price, date, surface, and rooms for an address or entire commune, covering 2021-2025, no API key needed.

Instructions

List actual notarized property sales (price, date, surface, rooms) recorded by the French tax administration (DVF) around an address, or for a whole commune if only a city is given. Data 2021-2025, no API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax sales returned (default 30)
yearsNoDVF years to include (2021-2025). Defaults to all.
addressYesAddress, street or city in France
radius_mNoSearch radius in meters around the address (default 300; ignored for city-wide queries)
type_localNoFilter by dwelling type
max_surface_m2No
min_surface_m2No

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the burden and adds useful behavioral context: data source (French tax administration DVF), time range (2021-2025), access requirement ('no API key'), and the address-vs-commune behavior. It does not disclose rate limits or result ordering, so it is not a 5.

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?

Two sentences front-load the core action, resource, and important access facts. Every phrase earns its place with no redundant schema repetition.

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?

The description covers what the tool returns (price, date, surface, rooms), data source, and coverage years. With no output schema, that return-field list is important and present; only minor operational details such as defaults/pagination are left to 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 71%, and the description adds context for 'address' and 'city' behavior but does not clarify the undocumented min/max_surface_m2 parameters. The description is not sufficient to fully compensate for the two schema-less surface filters.

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 names a specific verb ('List'), a precise resource ('actual notarized property sales ... recorded by DVF'), and the scope ('around an address' or 'whole commune'). This distinguishes it from estimation/price-per-m2 siblings.

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?

It clearly tells when to use the tool: for actual sales around an address or for a whole commune when only a city is provided. It does not explicitly name alternatives or exclusion conditions, but the 'actual notarized' phrasing contrasts with estimate-style siblings.

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