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

With no annotations, the description carries the behavioral burden and does disclose meaningful processing: grouping commas and currency are tolerated, and non-numeric rows are excluded and counted. It could go further by describing output format or behavior on missing/empty columns, but the key preprocessing traits are present.

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 dense sentence leads with the computed statistics and adds the key parsing caveats. There is no filler or repetition of schema fields.

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 one-parameter tool, the description is nearly sufficient: it lists the returned statistics and the data-cleaning behavior, and the schema defines the single input. It lacks an explicit output shape, but the enumerated statistics effectively communicate what the agent will receive.

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 provides only a required string named `column` with no description, so the description must add meaning. It does partly: the column is expected to contain numeric values, and it may include grouping commas or currency symbols. It does not specify valid column names or how to obtain them, but the sibling dataset_columns can fill that gap.

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 names the resource (numeric column of the Handovra dataset) and the exact operation (compute count, min, max, mean, median, sum). This clearly distinguishes dataset_stats from siblings such as dataset_columns, dataset_top, and dataset_row.

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: for summary statistics of a numeric column. However, no explicit guidance is given about when to prefer this tool over dataset_top, dataset_columns, or dataset_search, and no exclusions are stated.

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