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

The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, but the description adds meaningful behavioral context beyond these: it reveals the extraction-only constraint, the exact refusal reasons, the extra LLM call cost, and the structured return shape. This goes well beyond what annotations alone convey.

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 relatively long but densely informative: it front-loads the core purpose, then explains behavior, return values, refusal cases, use cases, and cost trade-off. Every sentence contributes actionable information for selecting and invoking the tool correctly. No filler is present.

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 documents the return payload and explicit refusal reasons, which is essential for a tool that must not hallucinate. It covers success and failure modes, use cases, cost implications, and routing behavior. With rich annotations and 100% parameter coverage, nothing critical 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 input schema already has 100% description coverage, explaining that all six parameters are aliases for the natural-language question. The tool description does not add any further parameter-level semantics beyond mentioning that the tool 'fills arguments' internally. Baseline 3 is appropriate because the schema carries the load.

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 this is a hallucination-resistant, grounded answer mode for high-stakes reads, and explains it routes to tools like ask_pipeworx but extracts answers strictly from tool results. It explicitly distinguishes itself from ask_pipeworx by emphasizing evidence and refusal behavior. A specific verb, resource, and unique behavior are all present.

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 explicitly says to use this tool whenever an answer will be quoted, cited, or acted on and the agent must not invent facts, while also instructing to prefer ask_pipeworx for casual lookups due to the extra LLM call cost. This gives clear when-to-use and when-not-to-use guidance relative to the sibling tool.

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