wb_ai_chat

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

With no annotations, the description carries the full burden and does well: it discloses the non-obvious gateway constraint (强制流式返回) and that the tool aggregates the stream into complete text, plus where reasoning-model thinking lands. It does not mention auth requirements or rate limits, so it stops short of a 5.

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?

Three sentences, zero waste: purpose first, dependency second, non-obvious return-behavior caveat last. Every clause earns its place and nothing is buried.

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?

There is no output schema, so the description must carry return semantics, and it does the essential part by explaining the streamed-then-aggregated response and the reasoning field. Remaining gaps (auth, failure modes) are minor for a straightforward completion call.

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 the schema already documents all six parameters including the enum and examples. The description only adds the source of model IDs, which is a modest increment over the schema's own example list.

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?

States a specific verb (完成一次对话补全) and resource (WorkBuddy AI 国际版任意模型), and explicitly routes the model argument to the sibling wb_ai_models, which distinguishes it from that sibling rather than confusing the agent.

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?

Tells the agent where model IDs come from (wb_ai_models), which is real routing guidance, but offers no explicit when-not-to-use-this or conditions under which the call would be wrong.

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