verify_reasoning

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the verification methodology ('ThoughtProof adversarial multi-model critique') and output details (verdict types, confidence score, objections), which adds useful context beyond basic functionality. However, it doesn't cover aspects like rate limits, authentication needs, or error handling, leaving gaps in behavioral understanding.

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 concise and front-loaded, stating the core purpose in the first clause. The second part efficiently lists the return values. There's no wasted text, but it could be slightly more structured (e.g., separating purpose from output details with a colon or bullet points) to enhance readability.

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's moderate complexity (3 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It explains the purpose and output format, which is helpful, but lacks usage guidelines and full behavioral context. Without an output schema, it should ideally detail return values more thoroughly, though it does list them briefly.

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 100%, so the input schema already documents all parameters thoroughly. The description doesn't add any additional meaning or context for the parameters beyond what the schema provides. This meets the baseline score of 3 when the schema handles parameter documentation effectively.

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: 'Verify a decision or reasoning claim using ThoughtProof adversarial multi-model critique.' It specifies the verb ('verify') and resource ('decision or reasoning claim'), and mentions the methodology. However, with no sibling tools, it doesn't need to differentiate from alternatives, so it doesn't reach the highest score of 5.

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, prerequisites, or exclusions. It only states what the tool does, not the context for its application. This lack of usage instructions limits its helpfulness for an AI agent.

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