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

Annotations only declare readOnlyHint and openWorldHint, so the description carries extra weight and delivers: it discloses the exact output composition (consensus, disagreements, confidence, combined answer) and a concrete cost of $0.04 USDC per call via x402. That cost/payment detail is genuinely useful behavior not in any structured field.

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?

Two tight sentences: the purpose and the multi-model mechanics come first, the cost second. No filler, though the parenthetical model list is dense.

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?

With no output schema, the description's rundown of the return payload is helpful, and the cost is covered. However, for a 3-parameter tool at 0% schema coverage, the undefined 'context' and 'async' parameters remain a real gap an agent must guess at.

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 0% for all three parameters (question, context, async), so the description must compensate and does not. It never mentions 'context' or 'async', leaving their purpose and impact undocumented.

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?

States a specific mechanism: three named models (gpt-oss-120b, Llama 3.3 70B, Gemma 4) answer independently, then a consensus, disagreements, confidence level and combined answer are produced. This clearly distinguishes it from sibling LLM tools like ask, answer and llm, which return a single model's response.

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 name and the phrase 'second opinion' imply the use case (cross-model validation), but the description never explicitly states when to pick this over ask/answer/llm, nor any exclusions. Usage is only inferred from the tool's name.

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