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

No annotations exist, so the description carries the full burden, and it does meaningful work: it discloses that grouping commas and currency symbols are normalized, and that non-numeric rows are both excluded and counted (a real data-loss behavior the caller should know about). It does not state permissions or error behavior for a fully non-numeric column, so it is strong but not exhaustive.

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 front-loaded sentence that leads with the returned metrics and then appends the two normalization caveats. Dense but every clause carries information; 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?

With no output schema, the description usefully enumerates the six returned statistics, and it covers data-cleaning behavior. The remaining gap is column-name provenance/validation and failure behavior on a non-numeric column.

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% for the single 'column' parameter, so the description must compensate, and it only adds the constraint that the column be numeric and belong to this dataset. It does not explain accepted column naming/format or where valid names come from (likely dataset_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?

Names a specific computation set (count, min, max, mean, median, sum) over a defined resource (a numeric column of the Outsourced IT Quotes dataset). It is clear and scoped, but it never references a sibling tool the way an ideal routing definition would (e.g. dataset_compare or dataset_top).

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 phrase 'of a numeric column' implicitly tells the agent this tool only applies to numeric columns, and the parenthetical about non-numeric rows hints at the expected data shape. There is, however, no explicit when-to-use, when-not-to-use, or alternative-tool guidance relative to siblings like dataset_top or dataset_compare.

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