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

With no annotations, the description carries the full burden, and it does disclose meaningful behavior: grouping commas and currency symbols are parsed, and non-numeric rows are excluded but still counted. It also enumerates the returned metrics. It omits what happens if the supplied column is entirely non-numeric or does not exist.

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?

One front-loaded sentence that leads with the returned metrics and relegates the data-cleaning caveats to a parenthetical. Dense but every clause carries information; nothing is padded.

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, listing the metric names in the description usefully substitutes for a documented return shape, and the cleaning rules cover the main edge case. Minor gaps remain around column-name validity and failure behavior for non-numeric columns.

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% for the single 'column' parameter, so the description must compensate. It implies the argument identifies a numeric column and that it belongs to this specific dataset, but never names the parameter, states the expected identifier format, or says whether the name must be exact.

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 the exact aggregate outputs (count, min, max, mean, median, sum) and scopes them to a numeric column of the Procedure Cost Checker dataset, so the operation is unambiguous. It does not explicitly contrast itself with siblings like dataset_top or dataset_row, so it falls short of a 5.

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?

Usage is implied rather than stated: the mention of a 'numeric column' signals this is for aggregate statistics rather than row lookup or listings. There is no explicit when-to-use or when-not-to-use guidance, nor any pointer to an alternative sibling for raw values.

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