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

With no annotations provided, the description carries the full burden of behavioral disclosure. It usefully reveals that grouping commas and currency formats are handled and that non-numeric rows are excluded and counted, which adds genuine behavioral context beyond the tool name. It does not describe the output structure, but the metric list substantially covers expected behavior.

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 compact sentence that front-loads the computed statistics and appends only essential parsing behavior in parentheses. There is no redundancy or wasted wording.

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 simple one-parameter statistics tool with no annotations and no output schema, the description covers the input expectation, the dataset scope, and the returned metrics. It does not explicitly describe the response format or error behavior, but the enumerated statistics are enough for an agent to understand what the tool provides.

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 provides no description for the 'column' parameter, so the tool description must compensate. It does by clarifying that the column must be numeric and that formatted values such as commas and currency are accepted. It could add guidance on where valid column names come from, but the single-parameter case is mostly covered.

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 tool computes summary statistics for a numeric column, enumerating exact metrics (count, min, max, mean, median, sum) and scoping it to the Curtilo dataset. This makes its purpose distinct from siblings like dataset_row, dataset_top, and dataset_columns without ambiguity.

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 intended use is implied: use this tool when you need numeric summary statistics for a column. However, it does not explicitly state when to prefer it over siblings such as dataset_top or dataset_row, nor does it provide exclusion criteria or alternative routing.

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