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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds substantial behavioral context beyond those: the exact success return shape with evidence/confidence/source/fetched_at, the explicit refusal contract with refusal_reason enum values, and the cost implication of an extra LLM call. There is no contradiction with the annotations.

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: mode definition, routing mechanism, extraction behavior, return contract, refusal reasons, use cases, and cost tradeoff. It is front-loaded with the core purpose and remains structured and scannable despite its length.

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 high complexity (routing across 5,743 tools) and the absence of an output schema, the description still defines both success and failure shapes in detail, enumerates refusal reasons, and clarifies the distinction from ask_pipeworx. An agent has enough information to select and invoke the tool correctly.

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 100%: all six parameters are clearly documented as aliases for 'question' in natural language. The description adds no parameter-specific semantics, but none are needed because the schema already fully covers the single logical parameter.

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 verb and resource: it is a 'hallucination-resistant answer mode' that extracts the answer using ONLY the tool result contents. It also explicitly contrasts itself with ask_pipeworx ('Same routing as ask_pipeworx') while differentiating via grounded extraction and refusal behavior, making it distinguishable from siblings.

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 cites high-stakes domains like financial verdicts, legal claims, medical lookups, and public statements. It also names the alternative and the tradeoff, telling the agent to 'prefer ask_pipeworx for casual lookups' because this mode costs one extra LLM call.

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