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

The description goes well beyond the readOnly/openWorld/idempotent annotations by disclosing the exact return contract, including the success shape and all refusal_reason enum values. It also reveals the extra LLM call cost and the strict 'ONLY what the tool result contains' extraction policy, which is critical behavioral 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: the core guarantee, routing behavior, return shapes, use cases, and cost/alternative tradeoff. The most important behavioral constraint is front-loaded, and length is justified by the high-stakes context and absence of an output schema.

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 no output schema, the description fully compensates by documenting both success and refusal response shapes, listing refusal reasons, and clarifying the tool's grounded extraction strategy. The inclusion of real-world high-stakes examples and the cost comparison makes the tool safe and complete for an agent to invoke correctly.

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% and the question parameter is already documented as accepting natural language with aliases. The tool description adds no significant parameter-level semantics beyond what the schema provides, so 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 clearly identifies the tool as a hallucination-resistant answer mode for high-stakes reads, with a specific verb and resource. It explicitly contrasts with ask_pipeworx by emphasizing extraction strictly from tool results plus explicit refusal behavior, making sibling differentiation immediate.

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?

Provides explicit when-to-use guidance: whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts. It also gives a clear when-not-to-use directive by saying casual lookups should prefer ask_pipeworx, including the cost tradeoff of one extra LLM call.

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