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

The description discloses important behavioral details: grouping commas and currency are handled, and non-numeric rows are excluded and counted. Since no annotations are provided, the description carries the full burden, and it does so well by explaining data cleaning behavior.

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?

The description is a single, well-structured sentence that front-loads the statistics list and then adds important caveats about data handling. Every word earns its place.

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 simple one-parameter tool with no output schema, the description covers the operation, data cleaning behavior, and the dataset context. It could mention the return format or error cases, but the description is largely complete for an agent to select and invoke it correctly.

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%, so the description must compensate. It explains the column parameter is a numeric column of the dataset, but does not specify exact column name format or how to discover valid columns. However, with only one parameter and a clear description, the meaning is mostly conveyed.

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 clearly states the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of a specific dataset. It distinguishes itself from siblings like dataset_row, dataset_search, and dataset_top by focusing on aggregate statistics rather than raw data retrieval.

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?

The description implies when to use this tool: when aggregate numeric summaries are needed, as opposed to row-level or search operations. It does not explicitly name alternatives or exclusions, but the context of sibling tools and the clear statistical scope provide adequate guidance.

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