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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently mentions that grouping commas and currency are handled, and that non-numeric rows are excluded and counted. These details go beyond the obvious functionality and inform the agent of edge-case handling. However, it does not describe the output format or behavior when the column is missing or entirely non-numeric, leaving some gaps.

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, tightly written sentence that front-loads the list of computed statistics and then appends the important handling details. Every phrase earns its place, with no filler or redundancy. It is concise and immediately informative.

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 only one parameter and no output schema, the description is quite complete: it lists all statistics returned, mentions handling of special formatting, and explains non-numeric row exclusion. It could be more complete by specifying the output structure or error behavior, but given the simplicity of the tool, it covers the essential information an agent needs to invoke it correctly.

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 input schema has no descriptions (0% coverage), so the description must compensate. It explains that the 'column' parameter refers to a numeric column of the Proofzo dataset and notes that formatting like commas and currency are handled, implying the column can contain such strings. This adds meaningful semantics beyond the bare schema definition, though it does not specify the exact data type or validation rules.

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's purpose: computing summary statistics (count, min, max, mean, median, sum) for a numeric column. It distinguishes itself from siblings like dataset_row or dataset_search by focusing on aggregation of a column, not row retrieval or search. The title and description align and leave no ambiguity about what resource and action are involved.

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 provides clear context for when to use the tool (numeric column statistics) but does not explicitly mention alternatives or when not to use it. While it implies usage for numeric analysis, it lacks an explicit comparison to sibling tools like dataset_top or dataset_compare. It is clear enough for an agent to infer appropriate use, but not as explicit as a named alternative.

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