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

With no annotations, the description carries the full burden, but it does disclose real behavioral traits: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. It leaves the return format, the handling of an all-non-numeric or nonexistent column, and the destination of the 'counted' non-numeric rows unexplained.

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 metrics before the parenthetical data-handling 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?

For a one-parameter, read-only aggregation tool with no output schema, the description covers the key facts: which metrics are returned and how dirty data is treated. Minor gaps remain around output shape and failure modes, but nothing essential for correct invocation is missing.

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 does add meaning by specifying the column must be numeric and that currency/comma formatting is tolerated, but it doesn't clarify name matching, case sensitivity, or error behavior for an invalid column.

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 concrete operation (compute count/min/max/mean/median/sum) on a specific resource (a numeric column of the Wedding Cost Checker dataset), so the agent knows exactly what it will get. It does not explicitly contrast itself with siblings like dataset_top or dataset_compare, but the enumerated metrics make its niche reasonably distinct.

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 tool versus alternatives such as dataset_top, dataset_compare, or dataset_search, and no prerequisites or exclusions. Usage is only implied by the metric list.

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