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

Beyond the readOnly and idempotent annotations, the description discloses the full refusal contract with enumerated refusal_reason values, guarantees verbatim evidence, and notes the extra LLM call. This is actionable behavioral detail an agent needs when interpreting failures or deciding whether to trust output.

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 first, then the return/refusal contract, then usage guidance and cost. Every sentence adds operational information, and there is no filler or repetition of schema/annotation content.

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 a single required parameter, no output schema, and annotations that already cover safety, the description is complete. It covers return shape, refusal modes, when to use the tool, the relationship to ask_pipeworx, and the extra cost, leaving no critical gap for an agent to call it 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?

The input schema already documents all six params as aliases for 'question' with 100% coverage, so the description does not need to add much. It does not provide deeper parameter semantics beyond what the schema states, so the baseline 3 applies.

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 clearly identifies a specific mode: 'Hallucination-resistant answer mode for high-stakes reads' and explains that it uses the same routing as ask_pipeworx but extracts answers only from tool results. This distinguishes it from its siblings, especially 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?

It explicitly states when to use the tool: 'whenever an answer will be quoted, cited, or acted on' and where facts must not be invented, with concrete domains like financial verdicts and legal claims. It also names the alternative and the cost tradeoff: '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.