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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds important behavioral context beyond that: extraction is limited to tool-result contents, refusals are explicit with enumerated reasons, and there is a cost tradeoff. No contradiction with annotations exists.

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 dense but every sentence earns its place: purpose, routing mechanism, success return shape, refusal shape, use cases, and cost tradeoff. It is front-loaded with the main distinction and contains no filler.

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?

There is no output schema, so the description correctly carries the burden of explaining return values—and it does, specifying both success and refusal object shapes plus refusal reasons. It also covers routing scale, source behavior, and when to prefer the cheaper sibling. Nothing essential for selecting or invoking the tool is missing.

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%—all six parameters are documented aliases for the same natural-language question. The description does not need to add parameter details, and it doesn't. It does explain the high-level question routing, but that is tool behavior, not parameter semantics, so the baseline score of 3 is appropriate.

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 opens with a specific capability—'hallucination-resistant answer mode for high-stakes reads'—and immediately contrasts with ask_pipeworx by explaining that it extracts answers using only the tool result. This clearly differentiates it from its grounded/un-grounded siblings without needing to inspect schemas.

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?

It gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on' and lists high-stakes domains. It also names the alternative and a decisive condition: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This is model-perfect usage guidance.

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