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

With no annotations provided, the description carries the behavioral disclosure burden and does so well: it reveals that grouping commas and currency are handled, and that non-numeric rows are excluded and counted. It does not describe exact error behavior or output structure, but for a read-only statistics tool these are meaningful additions.

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 sentence with no filler: the computed statistics are front-loaded, followed by the preprocessing behavior. Every clause earns its place by informing the agent what to expect.

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 aggregation tool with no output schema, the description covers the input domain, the computed fields, and handling of non-numeric rows. The main omissions are the explicit return format and behavior for missing or invalid columns, but these are minor for a tool this simple.

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 required string with no description, so the tool description must supply meaning. It clarifies that the column should be numeric and that comma/currency formatting is handled, which helps an agent pass an appropriate value. It stops short of specifying whether the value is a header name or whether matching is case-sensitive.

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 states a specific verb and resource: it computes count, min, max, mean, median, and sum for a numeric column of the Kbasevo dataset. This clearly separates it from sibling tools like dataset_row, dataset_top, and dataset_search, which have different retrieval 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 usage when aggregate statistics for a numeric column are needed, and the listed statistics provide clear context. However, it does not explicitly say when to prefer this tool over alternatives or mention any exclusions, so the agent must infer the usage boundary.

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