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

With no annotations provided, the description carries the behavioral disclosure burden. It usefully reveals that grouping commas and currency are handled and that non-numeric rows are excluded and counted. It could also mention edge cases like empty columns or output structure, but it is transparent about the main behaviors.

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 one compact sentence that front-loads the result metrics, then adds the input domain and behavior caveat. Every clause provides necessary information with 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?

There is no output schema, so the description compensates by listing the exact statistics the tool produces and key cleaning behavior. It does not explicitly state the return object shape or behavior on fully non-numeric input, but for a simple one-parameter stats tool it is nearly 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 schema has zero percent description coverage for the single `column` parameter, so the description must add meaning. It clarifies that the parameter should identify a numeric column of the Rotazo dataset and implies how non-numeric values are treated. It does not enumerate valid column names, but for a single string parameter this is sufficient.

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 resource (a numeric column of the Rotazo dataset), the operation (computing summary statistics), and the exact metrics returned. This clearly distinguishes it from sibling tools like dataset_columns or dataset_row.

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 makes the intended context clear: use this tool when you need count, min, max, mean, median, and sum for a numeric column. It does not explicitly name sibling alternatives or state when not to use it, so it stops short of a 5.

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