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

Annotations already declare readOnly, idempotent, and non-destructive hints. The description adds substantial behavioral detail beyond that: the tool returns a structured refusal object (with enumerated refusal reasons) when data doesn't contain the answer, extracts strictly from tool results, and returns verbatim evidence. This directly informs the agent of failure modes and constraints.

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?

Dense but efficient: purpose, mechanism, success/refusal return shapes, usage guidance, and cost tradeoff are packed into four substantive sentences. The most important information (hallucination resistance and high-stakes use) is front-loaded; no sentence is wasted.

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 must explain return values — and it does, enumerating both the success payload and the refusal payload with specific refusal reasons. It also provides routing context (5,724 tools across 1,497 sources) and the cost tradeoff, making the agent fully equipped to decide when to call 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 coverage is 100% — all six parameters are documented aliases for the same natural-language question. The description adds no new parameter-level detail, which is appropriate since the schema fully covers it. Baseline 3 applies because the heavy lifting is done by the schema.

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 leads with a precise purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It then explains the mechanism (routes like ask_pipeworx, fetches data, extracts answer only from tool result) and contrasts with the sibling ask_pipeworx by the grounded extraction and explicit refusal behavior. This makes the tool's role unmistakable.

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?

Explicit when-to-use guidance is given: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with concrete examples (financial verdicts, legal claims, medical lookups, public statements). It also states when to prefer the alternative: 'prefer ask_pipeworx for casual lookups' due to 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.