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

With no annotations present, the description meaningfully discloses behavioral details: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This goes beyond a bare statement of purpose, though it does not cover edge cases like null/empty values 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?

One focused sentence provides the core behavior first and then key parsing/exclusion details in a parenthetical. Every part adds value with 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 single-parameter numeric-column stats tool, the description covers the essential inputs, outputs, and notable parsing behavior. There is no output schema, but the listed statistics effectively describe the return values. Minor gaps include missing/null handling and exact output formatting, but these are not severe for this tool's simplicity.

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 non-empty string with 0% schema description coverage, so the description must carry the semantic weight. It adds that the column must be numeric and that currency/grouping formats are parsed, but it does not clarify column naming conventions or how to obtain valid column names. The parameter is simple enough that the lack of examples is not critical.

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 count, min, max, mean, median, and sum for a numeric column of the Lobbyvo dataset. It names the specific resource and output metrics, making it easy to distinguish from sibling tools like dataset_row or dataset_columns.

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 when to use the tool: whenever summary statistics for a numeric column are needed. However, it does not explicitly state when not to use it or point to an alternative, such as dataset_top for non-numeric summaries or dataset_search for filtering.

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