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

Annotations declare readOnlyHint=true and idempotentHint=true, and the description adds significant behavioral context: it returns a structured response with evidence, confidence, and refusal reasons, and discloses an extra LLM call cost. This goes well beyond the annotations and covers refusal behavior, which is critical for an agent to trust the tool.

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 every sentence earns its place: it covers purpose, routing, extraction behavior, return format, refusal reasons, usage guidance, and cost tradeoff. It is front-loaded with the core value proposition. It is slightly long but not bloated, so a 4 is warranted.

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 tool's complexity (refusal logic, multiple return fields, cost tradeoff), the description is fully complete. It explains the return structure, all refusal reasons, when to use it, and the alternative. With no output schema, the description carries the full burden and succeeds. Nothing essential 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 descriptions, including the alias behavior. The description adds no new parameter semantics beyond what the schema already provides; it merely reiterates the natural-language nature. Baseline 3 is appropriate 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 clearly states the tool's purpose: a hallucination-resistant answer mode that extracts answers only from tool results. It explicitly contrasts with the sibling ask_pipeworx, making it easy for an agent to distinguish the two. The verb and resource are specific and the purpose is not a tautology.

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 provides explicit when-to-use guidance ('Use whenever an answer will be quoted, cited, or acted on...') and when-not-to-use ('prefer ask_pipeworx for casual lookups'), naming the alternative tool. This is a textbook example of clear usage direction.

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