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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, and the description adds significant behavioral detail beyond that: grounded extraction, explicit refusal behavior with all refusal_reason values, and the cost tradeoff of one extra LLM call. 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 information-dense but every clause earns its place: purpose, routing behavior, return format, refusal reasons, use cases, and cost comparison are all packed efficiently. The sentence about preferring ask_pipeworx for casual lookups is a valuable routing rule, not fluff.

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?

Despite lacking an output schema, the description fully documents the success and refusal return shapes, enumerates all refusal_reason values, specifies when to use it, and flags the cost tradeoff. Given the complexity of a grounded-answer tool with no structured output schema, this is complete enough for an agent to invoke 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 schema already covers 100% of parameters, including the natural-language question and all aliases, so the high-coverage baseline of 3 applies. The description adds no parameter-specific guidance, but none is needed because the schema fully documents the single semantic input and its aliases.

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 purpose: 'Hallucination-resistant answer mode for high-stakes reads' and clearly explains how it works: it routes like ask_pipeworx, fetches data, then extracts the answer using only the tool result. It also distinguishes itself by naming the exact return envelope and refusal reasons, so the agent knows precisely what this tool produces.

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?

It explicitly states when to use it: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with concrete domains (financial verdicts, legal claims, medical lookups, public statements). It also provides the exclusion and alternative: 'prefer ask_pipeworx for casual lookups' and notes the extra LLM call cost.

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