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

Goes well beyond the readOnlyHint/openWorldHint annotations by disclosing that the answer is extracted ONLY from tool results, that the tool may refuse to answer with specific refusal_reason values, and that it costs an extra LLM call. These are material behavioral traits an agent cannot infer from annotations or schema.

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, routing behavior, success/refusal return shapes, usage guidance, and cost trade-off. The most important 'grounded, high-stakes' purpose is front-loaded before implementation details.

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 present, the description fully compensates by specifying the success shape, refusal shape, refusal reason enum values, and the key context of when to choose this over ask_pipeworx. An agent has enough to invoke it correctly and interpret the result.

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%, and the schema already explains that the single meaningful parameter is a natural-language question with aliases. The description adds no parameter-specific meaning, but none is needed because the schema fully documents it.

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?

Description names a specific mode ('Hallucination-resistant answer mode') and a concrete action: ask a natural-language question and get an answer extracted only from tool results. It distinguishes itself from sibling ask_pipeworx by emphasizing groundedness and the explicit refusal contract, so an agent can tell them apart immediately.

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?

Explicitly states when to use: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also names the alternative and the trade-off: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This gives clear when/when-not guidance.

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