ai_code_review

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

The description discloses the cost (50 sats per call) and indicates the tool checks for bugs, security, and performance issues. However, it does not mention whether the operation is read-only, what the output format is, or any state changes. Given no annotations, more detail on behavioral traits would be beneficial.

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 sentence plus a cost note, with no extraneous information. It is front-loaded with the core purpose and is appropriately sized for quick comprehension.

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 four parameters, the description fails to explain the return value (e.g., list of issues, score), language support, or invocation details like whether multiple files can be submitted. It is too sparse for the tool's complexity.

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 75% but the description adds no parameter details. The required 'code' parameter lacks a description in both schema and description, which is a significant gap. The description does not clarify how parameters like language or payment tokens affect behavior.

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 the tool performs code review for bugs, security, and performance issues, using specific verbs and resources. It distinguishes from sibling tools like ai_code_generate (code generation) and ai_explain (explanation).

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 explicit guidance on when to use this tool versus alternatives. The description does not mention prerequisites, limitations, or when not to use it. It only states what it does, leaving the agent to infer context from sibling names.

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