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

Beyond the annotations, the description discloses the exact success return shape, the refusal shape with all possible refusal_reason values, and the cost of one extra LLM call. It also emphasizes the key behavioral guarantee that the answer is extracted using only the tool result, which is valuable and non-obvious context.

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 covers purpose, mechanism, return contract, usage guidance, and cost trade-off. It is front-loaded with the core purpose and contains no filler or redundant restatement.

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?

Although there is no output schema, the description provides the full success and refusal response structures, including refusal reasons. Together with the annotations and clear usage guidance, an agent has everything it needs to decide when to invoke this tool and what to do with 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%, with every parameter documented as an alias for the natural-language question. The description adds no additional parameter semantics, which is acceptable since the schema fully covers the single meaningful input.

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 and states what it does: routes through Pipeworx, fetches data, and extracts an answer only from the tool result. It also differentiates itself from the sibling ask_pipeworx by its grounding requirement and explicit refusal behavior.

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 states when to use this tool: 'Use whenever an answer will be quoted, cited, or acted on' and gives concrete high-stakes examples. It also gives a clear exclusion by saying 'prefer ask_pipeworx for casual lookups,' naming the alternative directly.

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