generate_review_outline_data_v1

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

With no annotations, the description carries full burden. It discloses deterministic behavior ('无 LLM'), database write, rebuild flag for overwriting, and a clear return structure. This provides good transparency beyond the schema.

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 very concise: a brief purpose sentence, then Args and Returns sections. No wasted words, well-structured, and front-loaded with the core purpose.

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 tool has an output schema, the description does not need to detail return values, but it does. It covers all aspects: input choices, behavior, output. Complete for a straightforward tool.

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 coverage is 0%, so description must compensate. It adds meaning for all four parameters: topic and comm_ids are mutually exclusive, outline_style has a default, rebuild is a boolean. The return value is also described.

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 generates a reproducible review outline ('生成综述大纲（确定性，无 LLM）') from a topic or comm_ids, distinguishing it from potential LLM-based alternatives. The verb 'generate' and resource 'review outline' are specific.

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 two mutually exclusive inputs (topic or comm_ids) and optional parameters (outline_style, rebuild). It does not explicitly state when not to use or compare to siblings, but the context is clear enough for an agent to decide.

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