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

There are no annotations, so the description must carry the full burden. It mentions 'Ask humans' implying human involvement, but does not disclose that this is likely an asynchronous operation, whether it incurs cost, what the response format is, or how results are returned. Critical behavioral traits for an agent deciding to call it are missing.

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 with no fluff, making it concise and front-loaded. However, it is so brief that it omits essential information. Conciseness itself is good, but it borders on under-specification.

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 6 parameters, nested objects, no output schema, and no annotations, the description is far from complete. It does not explain the parameters, the return value, the asynchronous nature, or any operational details. An agent would struggle to call this tool correctly without external knowledge.

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 only 17% (only supply_policy has a description). The tool description implies the meaning of 'candidate_output' (AI output) and 'criteria' (criterion), but does not explain 'prompt', 'required_responses', 'max_cost_cents', or the structure of 'candidate_output'. Since the description does not compensate for the low schema coverage, this is a significant gap.

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 a clear purpose: 'Ask humans to verify an AI output against a criterion.' It names the verb (ask humans to verify) and the resource (AI output) and the criterion. It is not a tautology. However, it does not explicitly distinguish from sibling tools like me_approval or me_compare, so it misses the top 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?

No guidance is provided about when to use this tool versus alternatives. The description only states what it does, with no mention of scenarios, exclusions, or comparisons to sibling tools. The agent is left to infer usage from the name and description.

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