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

With no annotations, the description carries the full disclosure burden and does provide meaningful behavioral details: grouping commas and currency are normalized, and non-numeric rows are excluded but still counted. It doesn't mention error behavior for missing columns or the exact response structure, but the key quirks are covered.

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 statistic list and resource, then appends the important data-cleaning caveats. Every clause earns its place; there is no fluff or repetition.

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 statistical tool, the description covers the operation, the input's intended meaning, and the main dirty-data behaviors. The lack of an output schema is partially mitigated by explicitly listing the returned statistics, though exact return formatting and error conditions are left unspecified.

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 adds the crucial meaning that the column should be numeric and ties it to the Recallvia dataset. However, it doesn't specify how the column should be referenced (exact name vs. label) or what happens when the column doesn't exist, so compensation is only partial.

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 identifies a specific operation—computing count, min, max, mean, median, and sum—on a numeric column of the Recallvia dataset. This clearly distinguishes it from sibling tools like dataset_row, dataset_top, or dataset_compare, which handle different kinds of retrieval or comparison.

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 the tool: whenever aggregate numeric statistics for a column are needed. However, it never explicitly mentions alternatives or says when not to use it, so routing is left to inference rather than stated guidance.

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