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

Beyond the readOnly/idempotent annotations, the description discloses the grounded behavior in detail: it only uses tool result content, returns verbatim evidence, and can refuse with specific reasons like not_in_source or data_truncated. It also transparently states the potential extra LLM call cost and the refusal contract.

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 information-dense but every sentence earns its place: core behavior, return contract, refusal reasons, usage guidance, and cost tradeoff. It is front-loaded with the main purpose and each sentence adds decision-relevant value.

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 fully specifies the success and refusal return shapes, including evidence as a verbatim quote, confidence, source, fetched_at, and refusal reason enum. It also covers when to use and not use the tool, effectively making the tool self-contained for an agent despite its complex routing behavior.

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 schema already defines the 'question' parameter and its aliases. The description does not add parameter-specific detail, but it implies that the tool 'fills arguments' internally through routing, which is useful context. Still, it mostly relies on the schema rather than enriching the parameter semantics.

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 states a specific function: hallucination-resistant answer extraction for high-stakes reads, with a clear verb ('EXTRACTS the answer using ONLY what the tool result contains'). It names its close sibling 'ask_pipeworx' and contrasts with it as the grounded variant, making the differentiation explicit.

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: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with concrete domains (financial verdicts, legal claims, medical lookups, public statements). It also gives the when-not-to-use guidance: 'prefer ask_pipeworx for casual lookups' and notes the extra LLM call cost.

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