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

With no annotations, the description carries the full burden of behavioral disclosure. It transparently notes that grouping commas and currency symbols are parsed, and that non-numeric rows are excluded and counted. This gives the agent insight into edge-case handling. However, it does not state the output format (e.g., a JSON object with named fields) or behavior for empty columns, which would strengthen transparency.

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 that front-loads the core statistics list and places the additional handling details in a parenthetical. It is efficient and avoids redundancy, though the parenthetical is slightly dense. The structure is clear and easy to scan.

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 tool with one parameter and no output schema, the description covers the essential points: the dataset, the expected column type, the computed metrics, and special-case handling. It lacks an explicit statement about the return shape, but the list of statistics strongly implies a structured result. Minor gaps remain but the tool is largely self-explanatory.

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 generic 'column' parameter with no description (coverage 0%), so the description must add meaning. It does so by specifying that the column must be numeric and that formatted values (commas, currency) are handled. This goes beyond the schema and clarifies expected input semantics.

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 explicitly names the operation (summary statistics), the resource (a numeric column of the HardscapeDesk dataset), and the specific statistics computed (count, min, max, mean, median, sum). It also mentions handling of formatted numbers, which clearly differentiates it from sibling tools like dataset_row or dataset_top, which likely return raw rows or top values.

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 that the tool is used to obtain descriptive statistics for a numeric column, but it does not explicitly state when to prefer this over sibling tools like dataset_top or dataset_search, nor does it provide exclusion criteria (e.g., 'use dataset_row for individual records'). The context is clear enough for basic selection, but no alternatives are mentioned.

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