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

Even though annotations already indicate read-only, open-world, and idempotent behavior, the description adds substantial behavioral context: success response shape, refusal semantics with specific refusal_reason enum values, and the cost of one extra LLM call. It also explains the grounding guarantee—extracting only from tool results—which is not present in 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: purpose, routing behavior, success contract, refusal contract, use cases, and cost tradeoff. The most important differentiator—grounded extraction—is front-loaded, and the details are efficiently structured with semicolons and parenthetical lists.

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?

There is no output schema, so the description correctly takes on the burden of explaining return values and refusal reasons, and it does so exhaustively. Combined with the single-question input schema and clear use-case guidance, nothing an agent needs to decide when or how to call this tool 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%, and the schema itself fully documents the question parameter and all its aliases. The description adds no new parameter-level meaning beyond referring to the question input, so the baseline of 3 applies.

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 opens with a specific purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly describes the mechanism—'picks the right tool... fetches the data—then EXTRACTS the answer using ONLY what the tool result contains'—and differentiates itself from the sibling ask_pipeworx by its grounded extraction behavior.

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?

It gives 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 examples. It also names the alternative and the tradeoff: 'Costs one extra LLM call vs ask_pipeworx—prefer ask_pipeworx for casual lookups.'

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