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

The description discloses that it only extracts from tool results, refuses rather than invents, returns an explicit refusal object with enumerated refusal_reason values, and costs one extra LLM call. This goes well beyond the annotations, which already mark it read-only and non-destructive.

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 long but every sentence earns its place: purpose, behavior, refusal format, usage criteria, and cost comparison are all present without repetition. It is front-loaded with the core purpose before the details.

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?

For a tool with one required parameter and no output schema, the description fully compensates by specifying the success return shape, the refusal shape, refusal reasons, and selection guidance. An agent has enough context to invoke it correctly and interpret its result.

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%: all six parameters are described as the natural-language question or aliases. The description adds no parameter-level meaning beyond what the schema already provides, so the baseline of 3 applies.

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?

States a clear, specific purpose: a grounded, hallucination-resistant answer mode for high-stakes reads. It also differentiates itself from the sibling ask_pipeworx by describing the same routing but with extraction behavior, making tool selection unambiguous.

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?

Explicitly says when to use it: when an answer will be quoted, cited, or acted on, and when hallucination is unacceptable. It also gives a direct alternative preference: prefer ask_pipeworx for casual lookups, with the cost difference noted.

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