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

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description adds significant behavioral detail: the data source (DGFiP DVF actual geolocated sales), that it returns quartiles and volume, the exclusion of commercial properties, and the proxy nature for professional premises. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is dense but well-structured: first sentence defines main output, then exclusions, then source, then proxy note, then usage caveat. It is slightly long but every sentence adds value. Front-loaded with the core function and key constraints.

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

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

Given the tool returns multiple statistics (median, quartiles, volume) and has an output schema, the description sufficiently covers what the tool does, its limitations, source, and appropriate usage. It is complete for an AI agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already has 100% description coverage for all parameters (lat, lon, rayon_km). The description does not add new parameter-specific information; it only describes the overall output. Baseline score of 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the tool returns median residential property price per m², quartiles, sales volume, and period for a given point and radius. It explicitly distinguishes itself by specifying 'RÉSIDENTIEL bâti — maisons + appartements UNIQUEMENT, PAS les locaux commerciaux/professionnels', differentiating from tools that might return commercial prices or other real estate data. The verb 'coût du foncier' plus 'prix médian au m²' is specific and unambiguous.

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

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

The description provides clear context: it is for business case implantation but warns not to integrate into an attractiveness note. It also notes that for professional premises, the residential price is a proxy. However, it does not explicitly mention when to use this tool over siblings like 'dynamique_immobiliere' or others, though the exclusion of commercial properties implicitly guides usage.

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