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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable behavioral context beyond annotations: it returns a structured refusal with specific refusal reasons when the data does not directly answer, and it discloses the extra LLM call cost. It clearly states that the answer is constrained to the tool result, reinforcing the anti-hallucination promise.

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 clause earns its place, covering purpose, routing, output contract, refusal behavior, use cases, and cost trade-off. The key distinction from ask_pipeworx is front-loaded, and the usage guidance is actionable and immediately useful.

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 there is no output schema, the description fully describes the success response and the explicit refusal variants, including refusal reasons. It also covers routing behavior, data-fetching strategy, cost implications, and appropriate use cases. An agent has everything it needs to invoke the tool correctly and interpret the 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%, so the schema already documents the single required question parameter and five aliases. The description does not add meaningful parameter-level detail beyond saying the tool takes a natural language question, which the schema already conveys. This meets the baseline 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 this is a hallucination-resistant answer mode for high-stakes reads, distinct from the sibling ask_pipeworx. It explains the core behavior: routing to the right tool, fetching data, and extracting an answer solely from the tool result. This differentiates it effectively from the many sibling tools.

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 gives explicit when-to-use guidance: whenever an answer will be quoted, cited, or acted on and facts must not be invented, with concrete domains like financial verdicts, legal claims, and medical lookups. It also names the alternative, ask_pipeworx, and states when to prefer it due to the extra LLM call cost.

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