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

Beyond the annotations, the description discloses return shape, fields like evidence, confidence, fetched_at, and explicit refusal reasons such as not_in_source and data_truncated. It also surfaces the extra LLM call cost and the tool's strict 'ONLY what the tool result contains' behavior. No contradiction with the readOnly, openWorld, idempotent, or non-destructive 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 names the mode, explains the routing and evidence constraint, specifies success and refusal return shapes, gives concrete use cases, and ends with a cost-based routing tradeoff. It is front-loaded with the most decision-relevant line.

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?

With no output schema, the description compensates by fully documenting return values and refusal reasons. It covers the single required parameter through schema aliases, explains behavior, cost, and when to choose the sibling tool. Nothing an agent needs to invoke this tool safely and correctly is missing.

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%: every parameter is an alias for the same natural-language question field and the schema documents it thoroughly. The description adds no parameter-level meaning, but it does not need to because the schema already carries the full burden.

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 tool as a hallucination-resistant answer mode for high-stakes reads, and explicitly contrasts it with sibling ask_pipeworx by highlighting the same routing but stricter evidence-based extraction. This distinguishes it from ask_pipeworx and ask_pipeworx_beta without ambiguity.

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 an answer will be quoted, cited, or acted on and the agent must not invent facts. It also states the alternative and preference rule: prefer ask_pipeworx for casual lookups. This is clear, actionable routing advice.

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