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

With no annotations, the description carries the behavioral burden. It discloses meaningful behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. It does not describe the return structure, but for a read-only statistics tool the disclosed preprocessing behavior is valuable.

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 entire description is one front-loaded sentence: the statistic names come first, followed by a compact parenthetical covering data-cleaning behavior. Every clause earns its place with 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 single-parameter, no-output-schema tool, the description covers what the tool computes, which column it applies to, and how dirty data is handled. It stops short of specifying the exact response shape or behavior for a fully non-numeric column, but those are minor gaps.

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 input schema only defines column as a non-empty string, and schema description coverage is 0%. The description compensates by explaining that the column must be numeric and that comma/currency formatting is normalized, adding real semantic meaning to the parameter.

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) and the resource (a numeric column of the RoughInDesk dataset). It does not explicitly differentiate from siblings like dataset_top or dataset_compare, but the listed statistics make the tool's purpose unambiguous.

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 this tool is for numeric columns and that non-numeric rows will be excluded, giving the agent a sense of when it applies. It does not name alternatives or state when to prefer dataset_top or dataset_compare, so the guidance is implied rather than explicit.

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