Page Count Stats

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

No annotations exist, so the description carries the burden. It discloses important data-handling behavior: NULL pages_number values are excluded and counted separately, with a specific count of implausible values nulled. However, it does not describe other behavioral aspects such as defaults, filtering semantics, or output shape beyond what the schema implies.

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 concise and front-loaded, leading with the core relationship and then the exact question and key data caveat. Every sentence adds value, and no unnecessary detail is included.

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 an output schema and fully documented optional parameters, the description is largely complete: it explains the analytical focus, the key exclusion behavior, and the data-cleaning context. It lacks explicit guidance on when to prefer a sibling tool, but this is minor given the specificity of the description.

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 100%, so each parameter already has a clear description. The tool description adds no additional parameter-level meaning beyond the schema, keeping this at a baseline 3.

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 identifies the tool's purpose: relating book length (page-count quartiles/bands) to rating statistics, and explicitly poses the question it answers ('do longer books rate higher?'). This differentiates it from siblings like rating_distribution or stats_by_language, which focus on other dimensions.

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 a clear context for when to use this tool: when analyzing whether book length correlates with ratings. It does not explicitly name alternatives or state when-not-to-use, but the analytical question is specific enough to guide selection.

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