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

With no annotations, the description carries the full disclosure burden. It does reveal the shape of the result (averages, volumes, evolutions, conditional medians), which is useful behavioral context absent from any structured field. However, it says nothing about data coverage years, handling of missing communes, or error behavior, leaving notable gaps for a data-retrieval tool.

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?

A single dense sentence, front-loaded with the data source name and ending with the conditional output caveat. Nothing is wasted, though the heavy use of semicolons and abbreviations slightly reduces readability.

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 one required parameter, no annotations, and no output schema, the description does reasonably well by enumerating expected metrics. It falls short on parameter meaning and edge cases, leaving the agent without enough to call it confidently with correct commune codes.

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?

Schema description coverage is 0% and the single required parameter commune_code is not explained in the description at all — no mention that it is an INSEE code, no format guidance beyond the schema's min/max length. With the sole parameter undocumented in both schema and description, the description fails to compensate.

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 names the dataset (DVF), the level ('communal'), and the metrics produced (weighted average €/m², volumes, evolutions, type-level medians). This is a specific verb-plus-resource statement that distinguishes it from generic siblings like get_commune. It stops short of an explicit 'use this instead of X' contrast, which keeps it at 4.

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?

There is no explicit when-to-use or when-not-to-use guidance, and no comparison against the sibling tools such as get_commune or get_territory_scores. The only usage-adjacent hint is the conditional clause about medians being exposed by commune_market_summary, which implies a dependency but does not tell the agent when to pick this tool.

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