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

The description goes well beyond the annotations by revealing how the tool behaves: it performs routing and fetching, then extracts only from the result; it returns a structured success object with evidence and confidence, or an explicit refusal with enumerated refusal_reason values. It also discloses the extra LLM-call cost. Annotations (readOnlyHint, idempotentHint, openWorldHint, non-destructive) are consistent with this behavior, and no contradiction 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 first sentence immediately states the core value and audience. The body is dense but every clause earns its place: routing, extraction rule, success shape, refusal shape, usage scenario, and cost trade-off. Despite the length, it reads as one coherent, front-loaded definition rather than padded prose.

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?

There is no output schema, so the description fully compensates by specifying both the success return payload and the refusal payload with all refusal_reason values. It also covers when to use the tool, when not to, and the cost implication. For a complex high-stakes answer mode, this is complete enough for an agent to select and invoke 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 has 100% description coverage for the single natural-language question parameter and its aliases, so it already carries the semantic weight. The description adds contextual framing by mentioning that the tool fills arguments and fetches data, but it does not need to restate parameter details. Baseline 3 is appropriate because the schema, not the description, explains parameter semantics.

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, distinctive verb phrase: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly distinguishes this tool from sibling ask_pipeworx by stating that it extracts answers using ONLY the tool result content. The purpose—a grounded, evidence-based answering mode—is unmistakable.

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?

Usage guidance is explicit: '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 condition for choosing it: 'prefer ask_pipeworx for casual lookups.' This gives an agent a clear decision rule rather than leaving the choice to inference.

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