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 useful edge-case behavior: grouping commas and currency are handled, and non-numeric rows are excluded but counted. It says nothing about return shape, error behavior when the column does not exist, or dataset-size limits, so the disclosure is partial rather than rich.

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 that front-loads the returned statistics and relegates edge-case handling to a parenthetical. Nothing is wasted, though the nested parenthetical is slightly dense.

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 and no annotations, the description must also convey what comes back; enumerating the six statistics partially does this, and the row-exclusion note is valuable. It still omits output format, failure modes, and the column-identifier convention for a tool the agent must call blind.

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 a required, non-empty string), so the description must compensate. It adds the key constraint that the column must be numeric and that it belongs to the Taxooor dataset, but never states whether the value is a column name, header label, or index, leaving some ambiguity.

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) on a specific resource (a numeric column of the Taxooor dataset), so an agent knows exactly what it returns. It does not explicitly differentiate itself from siblings like dataset_top or dataset_compare, which also aggregate data, 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 reach for this tool versus dataset_top, dataset_compare, or dataset_columns. The numeric-column constraint implies the applicable scenario but is framed as a behavior note, not as usage guidance, and no exclusions or alternatives are named.

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