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

The description goes well beyond the annotations by disclosing the refusal behavior, including exact refusal_reason enum values (not_in_source, no_tool_match, tool_error, data_truncated, llm_error) and the success response shape. It also explains the extraction mechanism ('EXTRACTS the answer using ONLY what the tool result contains') and the cost tradeoff. The annotations (readOnlyHint=true, openWorldHint=true, idempotentHint=true) are consistent with the described behavior, with no contradiction.

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 well-structured: purpose is front-loaded in the first sentence, followed by response contract, refusal behavior, usage guidance, and cost tradeoff. It earns its length by packing in non-obvious behavioral details; only minor redundancy in the sibling reference and the refusal enum list could be trimmed.

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 complexity (one required parameter, rich response/refusal contract, multiple sibling modes), the description covers the full calling context: input format, routing behavior, output shape, failure modes, usage timing, and the reason to prefer the sibling. No output schema exists, so the description appropriately carries the return-value burden and does so completely.

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?

The input schema already provides 100% coverage by documenting question and all aliases, and the description reinforces the core semantic: a natural-language question. The description's grounded-answer contract adds context beyond the schema, explaining what the question parameter is for (routing to sources and extracting only from results), which makes the parameter's purpose clearer than the schema alone.

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, differentiated purpose: 'Hallucination-resistant answer mode for high-stakes reads,' clearly identifying the verb, resource, and distinguishing constraint. It explicitly names the sibling it is not ('Same routing as ask_pipeworx') and contrasts its behavior, so an agent can tell it apart from ask_pipeworx and ask_pipeworx_beta.

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 description gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' and lists concrete domains (financial verdicts, legal claims, medical lookups, public statements). It also provides a clear when-not-to-use instruction: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.'

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