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

With no annotations, the description carries the full burden, and it does disclose meaningful behavioral details: it handles grouping commas and currency, and excludes non-numeric rows while counting them. This goes beyond a generic 'get stats' statement. However, it does not specify the return format, behavior on empty columns, or what happens if the column does not exist.

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 well-structured sentence that front-loads the list of statistics and then packs in the key behavioral caveats. Every clause adds information; there is no filler or repetition.

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 core inputs and a couple of edge cases, which is adequate. But it leaves important gaps: the structure of the returned object, how the excluded non-numeric rows are counted (e.g., a separate 'excluded' field), and error behavior for missing or non-existent columns. The agent may be unsure what to expect from the output.

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 only defines 'column' as a string with no description, and schema coverage is 0%. The tool description compensates by explaining that the column must be numeric and that non-numeric values are handled in a specific way. This adds semantic meaning beyond the raw schema, though it could further clarify the exact column identifier format (e.g., exact name matching).

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 that the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Venbix dataset. This distinguishes it from the sibling tools (e.g., dataset_row, dataset_search, dataset_top) which are not aggregation tools. The verb-resource relationship is explicit and unambiguous.

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?

No guidance is provided on when to use this tool versus alternatives like dataset_search or dataset_top. There is no mention of exclusions, prerequisites, or scenarios where other tools would be more appropriate. The usage is only implied by the phrase 'numeric column', but no explicit when-to-use context is given.

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