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

There are no annotations, so the description carries the full burden. It discloses the exact output shape (10 grouped, open-ended questions), the pay-per-call payment requirement of $0.04 USDC on Base via x402, and the failure behavior when the payment-signature header is 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 core purpose is front-loaded in one sentence, followed by a routing sentence and payment details. There is no fluff or repetition of schema fields.

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?

The description gives output count, grouping, interview context, alternative tool, and payment authorization behavior. The only small gap is that although query is semantically necessary, the schema lists no required parameters and the description does not explicitly mark it as required.

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%, with both query and context already described in the schema. The description adds no additional parameter-level detail, so the baseline of 3 applies.

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 begins with a specific verb and resource: 'Write 10 open-ended discovery questions, grouped by category, for interviewing a customer for a full written case study.' It clearly distinguishes itself from the sibling generate-testimonial-questions by noting it is for full case studies, not short praise.

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?

'For short quotable praise, use generate-testimonial-questions' explicitly names the alternative and the condition that selects it. It also frames the intended context as discovery interviews for a written case study with problem, solution, and results.

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