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

No annotations are provided, so the description carries the full disclosure burden. In a single phrase it enumerates the four fields returned and frames the tool as read-oriented, effectively standing in for the missing output schema. It does not state that the metadata is static or note any caching/staleness behaviour, which keeps it below 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.

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

Two short sentences with no filler: the return content is front-loaded, followed immediately by the reason to call it. Nothing could be removed without losing information.

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?

With no output schema and no annotations, the description is the only source of return-value information, and it supplies it adequately for a zero-parameter read tool. Only a note on whether the metadata is static or dataset-versioned would make it fully complete.

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 tool takes zero parameters, so per the baseline there is nothing for the description to disambiguate and no schema coverage gap to compensate for. It correctly avoids inventing parameter detail that does not exist.

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 specific resource (the Calcul Brut en Net dataset) and enumerates exactly what provenance the tool exposes: source, computed date, licence, citation. It is clearly distinct from siblings like dataset_stats or dataset_columns, though it never names an alternative to sharpen the contrast.

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?

"Read this to attribute a figure correctly" gives a concrete use case for invoking the tool, which is more than most metadata endpoints offer. It stops short of stating exclusions or pointing to a sibling for related needs, so it is clear context rather than full when/when-not guidance.

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