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

No annotations are provided, so the description carries the behavioral disclosure burden. It usefully states that grouping commas and currency are handled and that non-numeric rows are excluded and counted. There is minor ambiguity about whether 'count' includes excluded rows, but the core behavior is transparent.

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 two compact sentences with the computed statistics listed first and no filler. Every clause adds meaningful operational detail.

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 one-parameter, read-only statistics tool with no output schema, the description covers the dataset scope, the exact statistics computed, and handling of formatted and non-numeric values. It does not specify the output shape or an all-non-numeric edge case, but these are not critical for selecting and invoking the tool.

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?

With schema description coverage at 0%, the description must compensate, and it does: it explains that the single `column` parameter refers to a numeric column in the Subsroom dataset and describes how non-numeric or formatted values in that column are treated. This goes beyond the bare string type in the schema.

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 concrete operation—computing count, min, max, mean, median, and sum—on a numeric column of a specific dataset. This clearly distinguishes it from sibling tools like dataset_row or dataset_search.

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?

It is clear that this tool is appropriate when summary statistics for a numeric column are needed, and the description notes how formatted numeric values are handled. However, it does not explicitly state when-not-to-use it or name alternative sibling tools.

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