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

Beyond the annotations (readOnly, idempotent, openWorld), the description discloses refusal behavior with specific refusal_reason values, the success return shape with evidence, and the extra LLM call cost. This gives the agent a clear model of what will happen when data is insufficient.

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: purpose, mechanism, return contract, usage boundaries, and cost tradeoff. It is front-loaded with the most important distinction and does not waste words on redundant schema 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, the description fully specifies the success and failure return shapes, including refusal reasons. It covers selection criteria, alternatives, behavior, and cost, making it complete for an agent to decide when to invoke this tool and what to expect.

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%, with the question parameter and all aliases fully documented in the input schema. The description adds little parameter-specific meaning, but it correctly implies the question drives tool routing and answer extraction, so a baseline score is appropriate.

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 states a specific purpose: 'Hallucination-resistant answer mode for high-stakes reads' that extracts answers only from fetched tool results. It also differentiates itself from the sibling ask_pipeworx by emphasizing grounded extraction and explicit refusals rather than open-ended generation.

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 lists high-stakes domains. It also tells the agent when not to use it: 'prefer ask_pipeworx for casual lookups', directly naming the alternative and the cost tradeoff.

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