ai_peer_review

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 mentions that feedback comes 'from Google Gemini' but does not describe key behavioral traits such as response format, latency, rate limits, authentication needs, or potential costs. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it operates.

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, well-structured sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to parse. Every part of the sentence earns its place by contributing essential information.

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 lack of annotations and output schema, the description is incomplete for a tool that involves AI feedback. It does not explain what the output will look like (e.g., structured feedback, scores, or text), nor does it cover behavioral aspects like error handling or limitations. For a tool with this complexity, more contextual information is needed to ensure proper usage.

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 has 100% description coverage, with clear documentation for both parameters ('user_question' and 'my_answer'). The description does not add any additional semantic context beyond what the schema provides, such as formatting examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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's purpose: 'Get peer review feedback from Google Gemini on your response to help improve accuracy and completeness.' It specifies the verb ('Get peer review feedback'), resource ('from Google Gemini'), and goal ('improve accuracy and completeness'). However, with no sibling tools mentioned, there's no explicit differentiation from alternatives, preventing a perfect score.

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 implies usage context ('on your response to help improve accuracy and completeness'), suggesting it should be used when seeking quality improvement for an answer. However, it lacks explicit guidance on when to use this tool versus other methods (e.g., self-review or other AI models) and does not specify any exclusions or prerequisites, leaving usage somewhat vague.

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