llm_code

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

No annotations are provided, so the description carries full responsibility. It mentions it is an AI code generation tool and its cost, but does not disclose any behavioral traits such as whether calls are safe (read-only), rate limits, authentication needs, or side effects. The brief description leaves significant gaps about the tool's behavior.

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 (one line) and front-loaded with the purpose. However, it includes pricing and an API URL that may not be necessary for an AI agent selecting a tool. The structure is minimal but not redundant.

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 description lacks information about the output or return value, and there is no output schema. Given the low complexity (2 parameters, no nested objects), the description should at least hint at what the tool returns (e.g., generated code). It also does not mention any constraints or typical use cases, leaving the tool's context incomplete.

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?

The input schema covers 100% of the parameters with Chinese descriptions. The description adds pricing and an API URL but no additional semantic context for the parameters. Since the schema already describes the parameters well, the description provides minimal added value, resulting in a baseline score of 3.

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 states '代码生成' (code generation) and includes pricing, so the general purpose is clear. However, it does not differentiate from sibling tools like llm_chat or llm_summary, nor specify the scope or capabilities of the code generation.

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?

No guidance on when to use this tool versus alternatives, no prerequisites, and no context for when not to use it. The description only provides pricing and an API URL, which does not help an agent decide when to invoke this tool.

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