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 key behaviors: grouping commas and currency are normalized, and non-numeric rows are excluded and separately counted. It does not cover return format or error behavior, but for a read-only statistical tool the main behavioral caveats are surfaced.

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 compact sentence that front-loads the computed metrics and uses a parenthetical to convey edge-case handling. There is no redundant wording; every clause adds information relevant to correct invocation.

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?

Given the tool's simplicity, one required parameter, and no nested schemas, the description is largely complete. It explains what is computed and how problematic rows are handled. It stops short of describing the exact output shape, but since no output schema exists, a brief note about return values would have made it fully complete.

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 schema only defines a single string parameter named 'column' with 0% description coverage, so the description must compensate. It does by clarifying that the parameter refers to a numeric column and noting how formatting and non-numeric values are treated. It could be more explicit about exact column-name formatting or case sensitivity, but the core meaning is clear.

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 identifies the operation: computing count, min, max, mean, median, and sum for a numeric column of the Fitouto dataset. It distinguishes itself from siblings like dataset_columns, dataset_search, and dataset_top by focusing on numeric summary statistics, though it does not explicitly name an alternative.

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 numeric columns and states that formatting is handled, which gives some contextual guidance. However, it does not explicitly explain when to prefer this over sibling tools like dataset_compare or dataset_search, nor does it list exclusions or prerequisites.

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