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

With no annotations, the description must carry behavioral disclosure. It usefully reveals that grouping commas and currency are handled and that non-numeric rows are excluded, but it leaves ambiguity about whether 'counted' means a separate count is returned, and it does not address missing-column or all-non-numeric edge cases.

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 focused sentence that front-loads the computed statistics and then adds only meaningful caveats about parsing and non-numeric handling. No filler or repetition exists.

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, it covers the core operation and important data-cleaning behavior. However, because there is no output schema, the description would benefit from stating the return structure and behavior when no numeric rows exist or when the column is missing.

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 has one undocumented 'column' parameter, and the description clarifies that it refers to a numeric column. It does not explicitly explain the expected column name format or how to discover valid column values, though the single self-named parameter is fairly self-evident.

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 count, min, max, mean, median, and sum for a numeric column of the Siftvo dataset. This makes its purpose distinct from sibling tools like dataset_row, dataset_top, or dataset_search.

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 context is clear: use this when summary statistics for a numeric column are needed. However, it does not explicitly mention alternatives or when not to use it, so the agent must infer the appropriate selection from the operation list.

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