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

With no annotations, the description carries the burden and does add useful edge-case behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. It omits error behavior for a missing/invalid column name and the exact shape/wording of the returned values.

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 sentence that front-loads the returned statistics and offloads edge-case handling to a compact parenthetical. No wasted words, though the sentence is dense rather than cleanly structured.

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?

No output schema exists, so the description correctly enumerates the returned statistics and covers data-cleaning behavior. It is nearly complete but does not address failure cases (missing column, empty dataset) that an agent may need.

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 0% description coverage on the single 'column' parameter, and the description only implies a column of the Calibvo dataset without specifying format (header name? index? case sensitivity?). It adds minimal meaning beyond the schema's string type.

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?

States the exact aggregate outputs (count, min, max, mean, median, sum) over a numeric column of the Calibvo dataset, which is specific enough to distinguish it from dataset_row/dataset_columns. It does not explicitly contrast itself with siblings like dataset_top or dataset_compare, 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?

There is no statement of when to use this versus alternatives (e.g. dataset_top, dataset_columns) and no prerequisites such as requiring a numeric column or what happens if the column is non-numeric. Usage must be inferred entirely from the description's content.

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