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

With no annotations provided, the description carries the burden of behavior disclosure. It reveals key parsing behaviors: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This gives meaningful insight into how messy data is processed, though it stops short of describing error cases or exact response structure.

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, efficient sentence that front-loads the computed metrics before adding parenthetical parsing details. Every element earns its place; there is no filler, repetition, or irrelevant context.

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 tool with one parameter, no annotations, and no output schema, the description covers the essential inputs, the expected column type, parsing behavior, and the list of results. The metrics list effectively serves as a return contract, though the exact JSON response format is not specified.

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?

The input schema defines 'column' only as a non-empty string with no property description, so schema description coverage is 0%. The description compensates by specifying that the parameter refers to a numeric column in the Clauselane dataset and by explaining how the column values are parsed, adding critical meaning the schema lacks.

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 title and description clearly state the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Clauselane dataset. The specific list of metrics and the explicit resource distinguish it clearly from sibling tools like dataset_columns or dataset_top, which serve different purposes.

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 the tool is for analyzing numeric columns by mentioning 'numeric column' and explaining how non-numeric rows are handled. However, it does not explicitly state when to use this tool over alternatives, nor does it provide exclusions or prerequisites, leaving routing mostly to inference.

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