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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description goes beyond these by disclosing the refusal behavior with specific refusal_reason values, the evidence/verbatim quote requirement, and the extra LLM call cost. No contradiction with annotations.

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, mechanism, success/refusal shapes, usage guidance, and cost comparison are all present. The list of refusal reasons and examples of high-stakes uses are justified. Slight redundancy in restating 'only what the tool result contains' after 'Hallucination-resistant' prevents a 5.

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 explains the return object and all possible refusal_reason values. It also covers when to use versus ask_pipeworx, the cost implication, and the natural-language input requirement. Nothing critical is missing for an agent to invoke this tool 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?

Schema coverage is 100%, with all six parameters documented as aliases for 'question'. The description adds no new parameter semantics beyond 'Your question in natural language,' which the schema already fully captures. Baseline 3 is appropriate when the schema does the heavy lifting.

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 states a specific purpose: 'Hallucination-resistant answer mode for high-stakes reads' with explicit behavior—extracting answers only from tool results. It clearly distinguishes this tool from the sibling ask_pipeworx by emphasizing grounded, evidence-based answers rather than casual lookup.

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?

Provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on' and lists high-stakes domains. Also gives an alternative: 'prefer ask_pipeworx for casual lookups' and explains the cost tradeoff of one extra LLM call, making the selection decision unambiguous.

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