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

No annotations are provided, so the description carries full burden. It states that a real human provides ratings, criteria scores, qualitative feedback, and comparison notes, which adds behavioral context. However, it lacks details on latency, authorization, or side effects, making it only moderately transparent.

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 two sentences, front-loaded with the core purpose, followed by usage guidance and output summary. Every sentence is necessary and concise, with no redundancy.

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 and the existence of an output schema (implied by 'Returns overall rating...'), the description covers purpose, usage, parameters, and return values. It misses details like expected turnaround time or cost, but is generally complete for decision-making.

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 100%, so baseline is 3. The description adds value by providing examples for targetDemographic (e.g., 'crypto-native traders, 18-35') and clarifying that context is optional for intent. This goes beyond the schema's parameter descriptions.

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 action: 'Get a real human matching your target demographic to rate and react to your content as a representative audience member.' It uses a specific verb and resource, and the mention of 'before A/B test commits, before ad spend, or before distribution decisions' distinguishes it from sibling tools like review_content or consult_domain_expert.

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 explicit use cases: 'Call before A/B test commits, before ad spend, or before distribution decisions.' This gives clear context for when to use the tool, but it does not mention when not to use it or explicitly list alternatives, which keeps it from a 5.

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