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

With no annotations, the description carries the disclosure burden and does well: it reveals that grouping commas and currency symbols are preprocessed and that non-numeric rows are excluded and counted. It does not disclose output structure or behavior for an entirely empty/missing column, but the stated cleaning behavior is significant.

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 stats first and then the edge-case behavior in a parenthetical. Every clause carries information, and there is no filler or duplication.

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, read-only statistics tool, the description covers the operation, input type, and key data-cleaning behavior. Since there is no output schema, a bit more detail about the returned format would be helpful, but overall the agent has enough to invoke it correctly.

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 column as a non-empty string with no description (0% coverage). The description adds that the column must be numeric and that formatted numbers (commas/currency) are accepted, which is genuinely useful. It still leaves the exact accepted column-name format unspecified, so it only partially compensates.

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 exact statistics (count, min, max, mean, median, sum) and the target (numeric column of the Jobcardo dataset), making its function unmistakable. It is clearly distinct from sibling tools like dataset_search, dataset_top, or 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 use case is implied by the focus on numeric-column summary statistics, but the description does not state when to prefer this over sibling tools or what to do when the column is non-numeric. No explicit exclusions or alternative routing are provided.

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