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

With no annotations provided, the description carries the full burden and does well by disclosing that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. It stops short of specifying the output structure or behavior for empty or absent columns, but the disclosed parsing behavior is valuable.

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 sentence that front-loads the computed statistics and resource, then adds a parenthetical with key parsing details. Every part earns its place; there is no filler 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 tool with one simple parameter and no output schema, the description covers the essential information: what is computed, on which column, and how edge cases like currency and non-numeric rows are handled. The missing exact return format and error behavior are minor for this level of complexity.

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 for the single 'column' parameter. It adds the important constraint that the column must be numeric and mentions currency/comma handling, which clarifies expected input. It does not provide examples or details about column name resolution, but the parameter itself is simple and self-descriptive.

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 operation: it computes count, min, max, mean, median, and sum for a numeric column of the Clipboardo dataset. This is specific and actionable, though it does not explicitly differentiate from sibling tools such as dataset_top or dataset_search.

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 phrase 'numeric column' implies the appropriate use case: when summary statistics of a numeric column are needed. However, there is no explicit when-to-use or when-not-to-use guidance, nor any mention of alternatives among the sibling tools, leaving the routing decision to inference.

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