Price per m² statistics (DVF)

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

With no annotations, the description carries the behavioral burden. It discloses the data source (actual notarized sales), the time range (2021-2025), and the computed metrics, but it does not mention output shape, missing-data behavior, or whether a minimum number of sales is required.

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 two sentences with no filler. It front-loads the core operation and adds only valuable context: source of data, statistics produced, geographic scope, and data years.

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?

The description is sufficiently complete for a statistics tool: it names the outputs (median, mean, quartiles, per-year evolution), the data source, and the accepted query scopes. It could add caveats about statistical confidence or sparse data, but nothing essential is missing given the detailed schema.

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 schema already describes all four parameters with 100% coverage, including defaults and the radius behavior for city-wide queries. The description adds no parameter-level meaning beyond that, so the baseline score of 3 applies.

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 uses a specific verb and resource: compute price-per-m² statistics from actual notarized DVF sales. It also specifies the exact statistics produced and the geographic scope (address or commune), making it distinguishable from sibling tools like property_sales or estimate_property.

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 implies the tool is for aggregate price-per-m² statistics and gives geographic scope, but it does not explicitly state when to prefer this tool over siblings or what conditions would make another tool more appropriate.

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