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

Annotations already mark this as read-only, open-world, idempotent, and non-destructive. The description adds meaningful behavioral detail beyond annotations: it explicitly returns either a grounded answer with evidence, or a structured refusal with specific refusal_reason values, and discloses the extra LLM call cost. 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 dense but every sentence earns its place: it opens with the core differentiator, explains the mechanism, enumerates the return/refusal contract, gives concrete use cases, and ends with the cost trade-off. No filler or repetition.

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, the description is fully self-contained: it explains routing, extraction constraints, success and failure shapes, refusal reasons, cost, and when to select the sibling alternative. An agent has everything needed to decide whether and how to invoke this tool.

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 coverage is 100% and every parameter is documented as an alias for question, so the schema carries the full semantic burden. The description adds no additional parameter-specific meaning, which is acceptable given the strong 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 identifies a specific verb and resource: an answer mode that fetches data through the same routing as ask_pipeworx but extracts answers only from tool results. It explicitly differentiates itself from ask_pipeworx, making it easy for an agent to understand what makes this variant distinct.

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?

Gives explicit when-to-use guidance: whenever answers will be quoted, cited, or acted on, and lists concrete domains like financial verdicts and legal claims. It also names the alternative, ask_pipeworx, and states when to prefer it, including the trade-off of one extra LLM call.

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