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

Annotations already mark readOnly and idempotent, but the description adds substantial operational transparency: the refusal contract with specific refusal_reason values, the verbatim evidence field, the guarantee to only use tool output, 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: the core purpose is front-loaded, followed by return shape, refusal behavior, usage guidance, and cost tradeoff. Every sentence adds actionable information without repetition or filler.

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?

Given there is no output schema, the description fully specifies both success and refusal return structures, lists the possible refusal_reason values, explains the routing behavior, and gives clear cost-based guidance. An agent has everything needed to select and call the 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% and the schema already fully documents the question parameter and its aliases. The description mentions routing and argument-filling at a high level but does not add new parameter semantics beyond what the schema provides. This meets the baseline for high schema coverage.

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 instantly identifies this as a 'Hallucination-resistant answer mode' and clearly distinguishes it from the sibling ask_pipeworx by emphasizing that it extracts the answer using ONLY what the tool result contains. It names the exact behavioral contract: returns an answer with evidence and refusal reasons. This is a specific, differentiated purpose.

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?

Explicitly states when to use this tool: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts'. It also gives the exclusion condition and alternative: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This is model decision guidance.

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