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

With no annotations, the description takes on the burden of behavioral disclosure and does so effectively by noting that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. This reveals parsing and data-cleaning behavior that could materially affect results, though null-handling and return format are not specified.

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 lists the output statistics first, then adds relevant caveats in a parenthetical. Every clause contributes necessary information, and there is no redundancy or 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 one-parameter tool without an output schema, the description covers input semantics and key behavioral edge cases such as currency handling and non-numeric rows. It does not describe the exact return structure, but enumerating the computed statistics gives the agent enough information to infer the expected result.

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?

Schema coverage is 0%, so the description must carry the meaning of the column parameter. It clarifies that the parameter refers to a numeric column of the Attestvio dataset, which is sufficient context for a single, simple string parameter despite not detailing column-name matching rules.

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 lists the exact statistics produced (count, min, max, mean, median, sum) on a numeric column of a specific dataset, making the tool's function unambiguous. This is clearly distinguished from sibling tools like dataset_row or dataset_top through the resource and operation described.

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 the tool is for obtaining summary statistics of a numeric column in the Attestvio dataset, but it does not explicitly state when to use it versus sibling tools or what conditions would make an alternative preferable. No exclusions or alternative routing are provided, so usage context is inferred rather than explicit.

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