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

With no annotations, the description carries the behavioral burden and does well: it discloses that grouping commas and currency are handled, and that non-numeric rows are excluded and counted. This gives useful edge-case behavior beyond the basic operation, though it does not specify output formatting or error 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 entire description is one dense, information-rich sentence. Every clause adds value: the statistic list, the dataset reference, the formatting handling, and the exclusion behavior. There is 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 simple one-parameter tool with no output schema, the description provides enough context to invoke it correctly: what to pass, what it returns, and some edge-case behavior. It could be more explicit about the exact response shape, but the enumerated statistics make the output predictable.

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 description coverage is 0%, so the description must compensate for the single 'column' parameter. It clarifies that the column should be numeric and that formatting quirks are handled, but it does not explicitly describe the parameter syntax or expected input format beyond that.

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 uses a clear verb context: it returns count, min, max, mean, median, and sum for a numeric column of the Enpso dataset. This distinguishes it from sibling tools like dataset_row or dataset_columns, which handle different operations.

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 makes the usage context clear: use it when you need summary statistics for a numeric column. It does not explicitly name alternatives or exclusions, but the context is strong enough to guide an agent toward appropriate use.

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