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

With no annotations provided, the description carries the behavioral disclosure burden. It does well by explaining that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. It does not detail output shape or error behavior, but the disclosed quirks are meaningful and non-obvious.

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 lists the computed statistics first and then adds relevant caveats. Every part carries information and there is no filler.

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 with no output schema, the description is largely complete: it names the dataset, the statistics, and important data-cleaning behavior. It could mention the return format or edge cases, but the core calling context is sufficiently covered.

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 documents a required string column with no description, and schema coverage is 0%. The description adds that the column must be numeric and belong to the OrderPadLedger dataset, which is useful, but it stops short of explaining exact column-name expectations or what happens for invalid columns.

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: computing count, min, max, mean, median, and sum for a numeric column of the OrderPadLedger dataset. This makes the tool's job concrete and distinguishes it from siblings like dataset_row or dataset_search, 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 should be used for numeric summary statistics on the OrderPadLedger dataset, but it does not explicitly say when to prefer this over siblings or when not to use it. There is no mention of alternatives or exclusions.

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