generate_content

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

No annotations are present, so the description carries the full burden. It discloses that output is based on real data from the knowledge base, that it produces a content skeleton rather than final polished content, and that items needing verification are marked. These are meaningful behavioral signals beyond the tool name.

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?

A single dense sentence front-loads the purpose, lists all supported output formats, and adds the important caveat that uncertain items are flagged. There is no filler or redundant repetition of schema information.

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?

The tool has moderate complexity with three parameters and no output schema. The description explains what kind of output to expect, the four possible formats, and the verification-marking behavior. It does not describe the exact returned structure, but combined with the complete schema, an agent likely has enough to invoke the tool successfully.

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 100%, with each parameter already described and an enum provided for format. The description does not add new parameter-level meaning beyond restating the available formats, so the baseline score of 3 is appropriate.

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—generating content from the 国威 knowledge base—and specifies the exact output types: 选题/长文/小红书/短视频口播. It is easily distinguishable from sibling tools like search_whisky and list_distilleries, which are retrieval-oriented rather than content-generation-oriented.

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 implies it is for producing content formats backed by real knowledge-base data, but it does not explicitly state when to use it over alternatives or when not to use it. No sibling tools are named, so the agent must infer the usage context.

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