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

Annotations already mark readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, but the description goes well beyond by disclosing the exact success return shape, the refusal schema, and specific refusal_reason enum values. It also transparently reveals the cost tradeoff of an extra LLM call, which is behavioral information not present in annotations or schema.

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?

Every sentence earns its place: purpose, routing comparison, success/refusal contract, use cases, and cost tradeoff. The structure is front-loaded with the core value proposition and then moves logically through behavior, usage, and alternative. Despite its length, there is no redundant or filler 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?

With no output schema present, the description fully compensates by detailing both the success response fields and the refusal response shape. It also covers when-refusal happens, when to prefer the alternative tool, and the behavioral cost difference. Nothing an agent needs to invoke and interpret this tool safely is missing.

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 schema already documents all six parameters at 100% coverage, each marked as an alias for 'question,' so the baseline is 3. The description adds no parameter-specific semantics beyond noting that the tool 'fills arguments' during routing, which does not meaningfully deepen understanding of the question parameter itself.

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 'Hallucination-resistant answer mode for high-stakes reads,' immediately defining the tool as a grounded answer mode with a specific verb-like purpose. It distinguishes itself from sibling ask_pipeworx by explicitly stating 'Same routing as ask_pipeworx' while adding that it extracts the answer 'using ONLY what the tool result contains.' This makes the tool's identity and differentiation unambiguous.

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 explicitly states when to use this mode: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete examples like financial verdicts and legal claims. It also gives a clear alternative and tradeoff: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This fully informs selection between the tool and its sibling.

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