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

With no annotations, the description carries the behavioral disclosure burden. It usefully discloses that grouping commas and currency formats are handled and that non-numeric rows are excluded and counted. However, it does not clarify output structure, behavior on an empty or fully non-numeric column, or whether 'count' refers to valid numeric rows, excluded rows, or both.

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?

A single front-loaded sentence lists the exact computed statistics and adds necessary parsing/exclusion caveats in a parenthetical. There is no redundant or filler content.

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, the description covers the core operation well. However, it lacks an output schema, does not specify the return format, and leaves edge-case behavior (empty columns, all non-numeric values, missing columns) ambiguous. More detail would make it fully reliable for an agent.

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 coverage is 0%, so the description must compensate for the single 'column' parameter. It does so by clarifying that the column should be numeric and that formatted values such as commas and currency are accepted. This gives meaningful interpretation beyond the bare string parameter.

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 count, min, max, mean, median, and sum for a numeric column of the RunbookDesk dataset. It is specific about the resource and operation, though it does not explicitly differentiate itself from sibling tools such as dataset_top or dataset_compare.

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 intended use is implied: use this when summary statistics for a numeric dataset column are needed. However, there is no explicit guidance on when to prefer this over siblings or when it would not be appropriate, such as for non-numeric or categorical columns.

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