column_statistics

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

The only behavioral trait mentioned is 'Read-only,' which is already declared by the readOnlyHint annotation. The description adds no new behavioral context beyond that, such as handling of non-numeric values or edge cases. Since annotations already cover the safety profile, this is acceptable but not enriched.

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 appropriately sized: a one-sentence purpose, a terse Args section, a Returns note, and a Read-only note. It is well-structured and front-loaded with the key information, with no unnecessary fluff.

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 simple analysis tool with a read-only annotation and an existing output schema, the description covers all necessary aspects: purpose, all parameters, return type, and safety. It is complete enough for an agent to select and invoke the tool 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?

Despite the schema having 0% description coverage, the description provides clear, meaningful explanations for all four parameters, including column as name or letter and has_header's purpose. This fully compensates for the schema's lack of descriptions.

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 a specific action: 'Compute descriptive statistics for a numeric column' and enumerates the exact statistics (mean, median, std, min, max, sum). This distinguishes it from sibling tools like value_counts or aggregate_data, though it doesn't name them explicitly.

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 gives clear context for when to use the tool: when you need descriptive statistics for a numeric column. However, it does not explicitly mention alternatives or when not to use it, so it stops short of a 5.

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