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

Beyond the read-only and idempotent annotations, the description discloses crucial behavioral traits: it uses ONLY the tool result content, returns an evidence verbatim quote, surfaces a structured refusal with specific reason values, and costs one extra LLM call. This is exactly the kind of behavioral context an agent needs to trust the answer and route appropriately.

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: purpose, routing mechanism, return contract, refusal taxonomy, use cases, and cost trade-off. It is front-loaded with the core capability and ends with pragmatic routing guidance, making it highly scannable for an agent.

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 and the absence of an output schema, the description fully compensates by specifying both the success and refusal response shapes, the exact refusal reasons, the grounding constraint, and the cost comparison with its sibling. There is no significant missing context that would cause an agent to misuse or misjudge 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%, so the input schema already fully documents every parameter as an alias for the natural-language question. The description does not add parameter-level detail, but it does not need to; the baseline of 3 applies because the schema carries the semantic load.

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, meaningful purpose — 'hallucination-resistant answer mode for high-stakes reads' — and clearly explains the mechanism: route through the same pipeline as ask_pipeworx, fetch data, then extract the answer strictly from the tool result. It also differentiates itself from ask_pipeworx by emphasizing evidence-backed answers and explicit refusals, so an agent can distinguish the two without inspecting the schema.

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 whenever the answer will be quoted, cited, or acted on, and when the agent must not invent facts, with concrete examples like financial verdicts and legal claims. It also names the alternative, ask_pipeworx, and instructs preferring it for casual lookups, making the decision boundary unambiguous.

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