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

With no annotations, the description carries the full burden and discloses the pay-per-use cost (7.0 USDC on Base), the premium nature, and the debate-then-conclusion process. It doesn't mention side effects or return format, but for a question-answering service the process and pricing are the key behavioral facts.

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 dense sentence that packs the process, models, input type, and pricing without fluff. It is front-loaded with the tool's core behavior, though the bracketed payment details are somewhat cryptic.

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?

For a single-parameter tool with no output schema, the description gives the essential invocation facts: input is a question, cost is 7.0 USDC, and the result is a judged conclusion. It is slightly incomplete only in lacking explicit output format and comparison to similar tools.

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?

Although schema coverage is 100%, the schema only describes input as 'service input.' The description adds real meaning by specifying that the input should be a question, which is essential for correct invocation.

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 specific behavior: multi-model debate between DeepSeek and Kimi followed by a judged conclusion. This distinguishes it from generic inference and research siblings, though it doesn't explicitly name an alternative or use an imperative verb.

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 only usage hint is 'input=question,' which implies what to pass, but there is no guidance on when to choose this tool over siblings like inference, deep-research-report, or research. No exclusions or alternative routing are provided.

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