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

With no annotations, the description carries the full behavioral burden, and it does disclose useful preprocessing semantics: grouping commas and currency are handled, and non-numeric rows are excluded and counted. It stops short of stating error behavior when the supplied column is non-numeric, or the result shape/pagination, so it is helpful but incomplete.

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, front-loaded with the computed statistics, with the preprocessing caveats tucked into a parenthetical. No wasted words and nothing important buried.

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?

There is no output schema, so the description correctly enumerates the return values and explains how rows are filtered. What remains unstated (error handling for a non-numeric column, dataset scope rigidity) is minor for a single-parameter read-only statistics tool.

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. It adds the meaningful constraint that the column must be numeric and belongs to the Strength Standards Calc dataset, but gives no guidance on column-name format or matching (e.g. exact vs case-insensitive).

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 the exact statistics returned (count, min, max, mean, median, sum) and scopes them to a numeric column of a specific named dataset, so an agent knows precisely what the tool produces. It does not explicitly contrast itself with siblings like dataset_top or dataset_compare, which keeps it 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 choose this tool over dataset_top, dataset_compare, or dataset_columns. Usage is only implied by the tool's return values; the description never says when it is or is not appropriate.

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