llm_chat

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

With no annotations provided, the description carries full disclosure burden. It adds pricing and a free tier note, which is useful, but fails to disclose rate limits, authentication requirements, response format, or any side effects. Behavioral insight is minimal beyond cost.

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 short line, but it includes both the core purpose and supplementary details (pricing, API link). It is front-loaded with the tool function. However, the pricing and link info could be structured separately for clarity.

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 many sibling tools, the description should explain the return format or expected output. It does not. Additionally, usage context is missing. For a simple two-parameter tool, the description is incomplete for proper invocation and result handling.

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%, and the schema already describes both parameters ('prompt' and 'model' with examples). The description does not add any extra semantics beyond what the schema provides, so baseline 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 'AI大模型对话生成' clearly indicates the tool generates chat responses using AI models. The name 'llm_chat' reinforces this. However, there is no explicit differentiation from sibling tools like 'llm_code' or 'llm_summary', though the chat-specific verb and resource are clear.

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 no guidance on when to use this tool versus alternatives such as 'llm_code' or 'llm_translate'. There is no mention of prerequisites, scenarios, or exclusions, leaving the agent to infer usage from the name alone.

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