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

The description goes far beyond the annotations by disclosing the return contract, the explicit refusal object, the refusal reasons, the extra LLM call cost, and the hard constraint that answers use only the tool result. It also explains the evidence field is a verbatim quote. None of this is available from the annotations alone, and it gives the agent an accurate model of how the tool behaves.

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: purpose, mechanism, return contract, refusal behavior, use cases, and cost/alternative are all covered. The most important distinguishing fact is front-loaded, and the guidance ends with a practical preference rule. 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 there is no output schema, the description fully compensates by specifying the success and refusal response shapes, the meaning of evidence, and the possible refusal reasons. It also covers selection logic, cost trade-offs, and when to prefer a sibling. 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 description coverage is 100%: all six parameters are documented as aliases for the natural-language question, with the main 'question' parameter fully explained. The description adds no parameter-level detail beyond this, but it does not need to because the schema already carries the full semantic load. Baseline 3 is appropriate.

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 opens with a specific, differentiating purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly identifies the tool as a grounded variant of ask_pipeworx and explains its exact behavior: selecting a tool, fetching data, and extracting answers only from the result. This is more than a generic statement and cleanly separates it from siblings like ask_pipeworx and ask_pipeworx_beta.

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: use when answers will be 'quoted, cited, or acted on' and the agent must not invent facts, with concrete domains listed. It also names the alternative, ask_pipeworx, and gives a clear preference rule: 'prefer ask_pipeworx for casual lookups.' This is exemplary routing guidance.

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