Review-Code

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool builds prompts for code review and scoring without directly calling the LLM, which implies it's a read-only or preparatory operation. However, it lacks details on permissions, rate limits, output format, or any side effects. For a tool with 3 parameters and no annotations, this is insufficient behavioral context.

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, efficient sentence in Chinese that clearly states the tool's function and a key constraint (not directly calling LLM). It is front-loaded with the main purpose and has no wasted words, making it appropriately concise for the complexity level.

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 3 parameters, no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It explains the high-level purpose but misses critical details like parameter meanings, behavioral traits, and output expectations. For a tool that likely generates structured prompts, more context is needed to guide effective use.

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 0%, so the schema provides no parameter descriptions. The tool description does not mention any parameters ('code', 'style', 'commitMessage') or their meanings. With 3 parameters and no information in either the schema or description, the agent has no semantic guidance beyond the parameter names. This fails to compensate for the low schema coverage.

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 the tool '构建用于代码整体审查与打分的 LLM 提示词（不直接调用 LLM）' which translates to 'Builds LLM prompts for overall code review and scoring (does not directly call LLM)'. This provides a clear verb ('builds') and resource ('LLM prompts'), but it's somewhat vague about the exact nature of the prompts and doesn't distinguish from siblings like 'review_diff' or 'review_file'. The purpose is understandable but lacks specificity.

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 like 'review_diff' or 'review_file'. It mentions that it does not directly call the LLM, which hints at a preparatory step, but there's no explicit when-to-use or when-not-to-use context. Usage is implied rather than stated, leaving gaps for the agent to infer.

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