Get User Stats

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

Annotations already indicate readOnlyHint and openWorldHint. The description adds rich behavioral details: paginated fetch, local aggregation, specific output fields including truncation warning. This exceeds mere annotations.

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 paragraph that is well-organized with clear sections: main purpose, process, output fields, and edge case. It is informative but could be slightly more concise by removing some redundancy (e.g., listing all fields twice in text).

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 no output schema and no parameters, the description must fully describe the return value. It does so comprehensively, listing all structured fields and the truncation behavior. The annotations (read-only, open-world) complement 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?

There are no parameters (input schema empty), so schema coverage is 100% vacuously. Per guidelines, baseline for 0 params is 4, and the description fully explains what the tool does without needing parameters. It adds thorough output semantics.

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 verb 'compute' and the resource 'summary statistics over the authenticated user's entire anime list.' This distinguishes it from sibling tools like get_my_anime_list (which fetches raw data) and analyze_taste (likely different analysis).

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 explains the process (one paginated pass, local aggregation) and implies it is the comprehensive stats tool. While it does not explicitly say when to use vs alternatives, the context signals and sibling names make it clear. Slightly lacking explicit usage boundaries.

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