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

With no annotations, the description carries the full behavioral burden. It helpfully discloses that grouping commas and currency are handled and that non-numeric rows are excluded and counted, but it does not state that the operation is read-only, how permissions work, what happens on errors, or how the results are structured.

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 a single well-formed sentence that front-loads the statistics list, then the data source, then behavioral caveats in parentheses. Every clause carries information and there is no filler.

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 tool with no output schema, no annotations, and one required parameter, the description covers the core statistics and two data-cleaning behaviors. However, it omits output shape, error handling, empty-result behavior, and column-discovery guidance, leaving meaningful gaps for correct invocation.

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%, so the description must compensate for the single undocumented 'column' parameter. It adds an important constraint by specifying that the column must be numeric and belong to the Calcul Brut en Net dataset, but it does not explain valid column names, format expectations, or how to discover columns.

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 exact statistics computed and the data source (a numeric column of the Calcul Brut en Net dataset), making the tool's purpose unambiguous. It does not explicitly contrast with sibling tools like dataset_compare or dataset_top, but the numeric-column scope is distinctive enough to separate it from most 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?

There is no when-to-use guidance, no mention of prerequisites, and no named alternative tool. The description only states what is returned, leaving the agent to infer when this tool is appropriate versus siblings such as dataset_columns or dataset_top.

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