stats_summary

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

The description details the specific statistics returned and emphasizes the single-call convenience, which is useful behavioral context. However, with no annotations, it does not address edge cases, error handling, or processing assumptions, leaving gaps in transparency for a pure computation tool.

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?

Two concise sentences: the first establishes the purpose, the second lists the output metrics. There is no redundancy, and the key information is front-loaded.

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?

Given the simple one-parameter schema and the presence of an output schema, the description sufficiently covers the core functionality and return values. However, it lacks sibling differentiation and any notes on when to use this over related tools, so it is not fully complete in context.

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 description mentions 'dataset' but provides no additional meaning about the data parameter beyond the schema's array-of-numbers definition. With 0% schema description coverage and a single parameter, the description should have compensated by explaining format expectations or constraints, but it does not.

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 it computes a comprehensive statistical summary, listing specific metrics such as count, mean, median, and percentiles. This distinguishes it from siblings like 'percentile' which focuses on individual percentiles, and 'statistics_analysis' which may be broader.

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?

No explicit guidance on when to use this tool versus alternatives like 'statistics_analysis' or 'percentile'. The phrase 'in a single call' implies a convenience benefit, but there is no direct comparison or exclusionary guidance.

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