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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, but the description adds significant behavioral detail beyond those: it explains the underlying routing and tool-selection mechanics, specifies that answers are extracted strictly from tool results, defines the exact success and refusal return shapes with all refusal_reason values, and notes the extra LLM call cost. This fully discloses how the tool behaves in edge cases where 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 information-dense but every sentence earns its place: purpose, mechanism, return format, refusal reasons, usage scenarios, and cost comparison. It is front-loaded with the most important differentiator ('Hallucination-resistant') and structured so an agent can quickly extract the key behavioral and routing facts without parsing 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 the absence of an output schema, the description thoroughly documents the return structure with both success and refusal variants, lists all possible refusal_reason values, and explains when those refusals occur. The parameter schema is complete and the usage guidance is explicit. Combined with the annotations covering safety and idempotence, nothing necessary for correct invocation 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 covers 100% of parameters with detailed descriptions, including aliases for 'question', so the baseline is 3 per the rubric. The tool description does not add new parameter semantics beyond the schema, but it does not need to; it focuses on behavior and return format instead. There is no contradiction or gap in parameter understanding.

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 verb and resource: 'Hallucination-resistant answer mode for high-stakes reads' that 'EXTRACTS the answer using ONLY what the tool result contains.' It clearly distinguishes itself from the sibling ask_pipeworx by emphasizing groundedness, evidence, and explicit refusals rather than a casual lookup. An agent can immediately understand what this tool does and how it differs from its sibling without opening schemas.

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 the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements).' It also names the alternative and the condition for choosing it: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This is clear routing between siblings.

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