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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive, but the description goes well beyond that: it explains the tool only uses tool-result content, returns a specific success/refusal contract, lists refusal reasons, and reveals the cost of one extra LLM call. This gives the agent an accurate behavioral model.

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 longer than average but every sentence earns its place: purpose, routing, success contract, refusal contract, use cases, and cost tradeoff are all packed efficiently. The key differentiator ('hallucination-resistant') is front-loaded, though the length prevents a perfect score.

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 having no output schema, the description fully specifies the return shape and all refusal reasons. It covers the routing behavior, the extra-call cost, and the high-stakes contexts where this mode is appropriate. An agent has enough to decide whether to invoke it and what to expect back.

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 description coverage is 100% and includes alias handling for the single required question parameter. The description mentions argument filling at a high level but doesn't add new parameter-level semantics beyond the schema, so the baseline of 3 is appropriate.

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 identifies this as a hallucination-resistant answer mode that routes like ask_pipeworx but extracts answers strictly from tool results. It distinguishes itself from the sibling ask_pipeworx by emphasizing grounded answers, evidence, and explicit refusals, so an agent can tell them apart.

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 this tool ('whenever an answer will be quoted, cited, or acted on'), gives concrete domains (financial verdicts, legal claims, medical lookups, public statements), and advises preferring ask_pipeworx for casual lookups due to the extra LLM call. This is clear when-to-use and when-not-to-use guidance.

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