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

With no annotations and no output schema, the description carries the full disclosure burden, and it does describe the payload contents (columns, numeric flags, row count, provenance banner). It never explicitly states read-only/safe semantics, but for a zero-parameter inspection call the return-content disclosure is the substance an agent needs.

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 sentences with zero filler: the return contents are front-loaded, followed immediately by the usage directive. Nothing is repeated from the name or title.

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?

For a simple no-param, no-annotation, no-output-schema read tool, describing the returned fields is essentially complete coverage. It could go one step further by clarifying how it differs from dataset_stats or dataset_provenance, whose outputs may overlap on row count and provenance.

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 no parameters, so by the rubric this is a baseline 4; there is nothing for the description to clarify beyond what the empty schema already implies.

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 a specific resource (the Calcul Brut en Net dataset) and enumerates exactly what is returned: column list, which columns are numeric, row count, and the provenance banner. It implicitly separates itself from dataset_stats and dataset_provenance by scoping to schema shape, though it never names those siblings.

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?

"Call this first to learn the schema" gives concrete ordering guidance for when to invoke the tool relative to the rest of the dataset family. It stops short of naming alternatives or stating when not to use it, so it is clear context rather than full routing.

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