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

Beyond the read-only/idempotent annotations, the description discloses the internal routing behavior, the strict grounding guarantee, the exact success/refusal response shapes, and the enumerated refusal_reason values. It also reveals the cost implication of one extra LLM call. This goes far beyond the 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 and front-loaded with the primary differentiator, and every clause adds information about behavior, output, or usage. It is slightly long and mixes return-schema details with usage guidance, but it earns its length given the complexity of the tool.

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 covers return values, including both success and refusal structures and every refusal reason. It also covers routing, grounding, cost, and alternative selection, leaving no critical gap for an agent deciding how to call it.

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 parameters are documented as aliases for the natural-language question. The description does not add any new meaning about the question parameter itself (e.g., length, format, constraints). It only reinforces that the question will be routed, which is already implied by the tool's purpose. 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?

Opens with a specific value proposition ('Hallucination-resistant answer mode for high-stakes reads') and a concrete verb ('EXTRACTS the answer using ONLY what the tool result contains'). Explicitly distinguishes from sibling ask_pipeworx by describing the same routing but a grounded extraction step, making it clear what this variant adds.

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?

Gives explicit when-to-use guidance: whenever the answer will be quoted, cited, or acted on and fact-invention is unacceptable, with concrete examples. Also provides the when-not-to alternative: 'prefer ask_pipeworx for casual lookups' and explains the trade-off (extra LLM call).

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