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

With no annotations, the description carries the full disclosure burden, and it does add real behavioral context: grouping commas and currency symbols are parsed, and non-numeric rows are excluded but counted. It stops short of describing the response shape, where the excluded-row count is surfaced, or what happens if the named column does not exist or is non-numeric.

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?

A single sentence front-loads the exact outputs and then packs clarifying preprocessing behavior into one parenthetical. No filler text, though the parenthetical could have been split for readability.

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, no-annotation, no-output-schema tool, the description covers what is computed and how messy input is handled. The main remaining gap is that it says non-numeric rows are 'counted' without explaining where that count appears in the result.

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 coverage is 0% - the schema only declares 'column' as a non-empty string. The description compensates partially by implying the column must be numeric and must belong to the Stocktaka dataset, and by explaining that non-numeric values are tolerated rather than erroring. It still does not clarify valid column naming, case sensitivity, or whether the column name is validated against the dataset.

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 computation (count, min, max, mean, median, sum) against a specific resource (a numeric column of the Stocktaka dataset), so an agent immediately knows what it produces. It does not explicitly differentiate itself from siblings like dataset_top or dataset_compare, but the operation is distinct enough to be inferred.

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 statement of when to use this tool versus alternatives, nor any prerequisites or exclusions. The agent must infer that it is for numeric aggregation rather than, say, dataset_top or dataset_compare, based purely on the task wording.

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