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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses substantial behavior: it refuses rather than fabricates, returns a structured success/refusal envelope, includes verbatim evidence, and names the refusal reasons. It also surfaces an extra LLM call cost, which is exactly the kind of behavioral trait annotations do not capture. 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 and front-loaded with the key differentiator—hallucination resistance—before moving to mechanics, output contract, usage, and cost. Every sentence contributes distinct information and none merely restates the name or schema. Despite length, it is efficient and well structured.

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?

For a tool with no output schema, the description fully compensates by defining the success return fields and the explicit refusal schema. It covers routing behavior, source scope, usage context, cost trade-off, and relationship to siblings. An agent has everything needed to decide when to call it and what to expect back.

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%, and the schema already documents the question parameter plus all five aliases with their exact behavior. The description adds little parameter-specific meaning beyond framing the question as natural-language and emphasizing grounded extraction. The high schema coverage sets the baseline at 3, and the description does not meaningfully exceed 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?

The description identifies a specific mode—'Hallucination-resistant answer mode for high-stakes reads'—and ties it to a clear mechanism: extracting answers only from tool results. It explicitly distinguishes itself from the sibling ask_pipeworx by describing the same routing but a stricter answer-extraction behavior, so an agent can tell them apart.

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,' with concrete domains like financial verdicts and legal claims. It also names the alternative and the circumstance to prefer it: '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.