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

There are no annotations, so the description carries the burden. It discloses that non-numeric rows are excluded and counted, which is useful behavioral context. It doesn't mention edge cases like all-non-numeric columns or potential errors, but the disclosed behavior is a solid start.

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, information-dense sentence that front-loads the list of statistics and then clarifies data handling. It's concise and every clause earns its place, though it could be slightly clearer with a period after the parenthetical.

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 tool, the description covers the key computation and data cleaning behavior. It lacks details on return format, but with no output schema, a brief mention of what the output looks like could improve completeness. Overall, it's adequate for a simple stats tool.

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%, but the description explains the meaning of the column parameter by stating it must be a numeric column and mentions handling of formatting. This adds meaning beyond the schema's minimal type and minLength, but doesn't fully specify the expected format or constraints.

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 summary statistics (count, min, max, mean, median, sum) for a numeric column, which is a specific verb and resource. It could be distinguished from siblings like dataset_top or dataset_search, though it doesn't name them explicitly, so it loses a point for not differentiating.

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 usage for numeric columns and mentions handling of currency and grouping commas, giving context. However, it doesn't explicitly state when to prefer this over sibling tools or provide exclusions, so guidance is only implied.

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