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

With no annotations, the description carries the full behavioral disclosure burden. It does well by noting that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which are non-obvious data-cleaning behaviors. It does not mention return structure or empty-column behavior, but the listed statistics largely imply the output.

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?

One dense sentence delivers the operation, resource, and important edge-case handling without wasted words. The parenthetical qualifiers earn their place because they change how an agent should interpret inputs and results.

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 single-parameter, no-output-schema tool, the description is nearly complete: it names the input, the output statistics, and the key data-cleaning behavior. It lacks explicit error handling or return-format details, but those are minor for this simplicity level.

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, and the description does not name the parameter explicitly. However, it adds real meaning by specifying that the column must be numeric and that formatting/currency quirks are handled, which helps the agent choose an appropriate value.

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, sum) and the resource (a numeric column of the Walkvo dataset). It does not explicitly differentiate from siblings, but the aggregate-statistics nature is self-evident against row/search/top tools.

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?

Usage context is implied: an agent would call this when it needs summary statistics for a numeric column rather than row-level or search operations. No explicit when/when-not guidance or named alternatives are provided, leaving the choice to inference.

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