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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, and the description adds substantial behavioral detail beyond that: it extracts answers only from the tool result, refuses rather than guesses, returns a structured object with evidence and confidence, and lists specific refusal reasons. It also discloses the extra LLM call cost, which is exactly the kind of operational behavior an agent needs to know.

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: what the tool does, how it works, return shape, refusal shape, when to use it, and when not to use it. It is front-loaded with the most decision-relevant information, and the cost tradeoff is placed last as a secondary consideration.

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?

For a tool with no output schema, the description fully compensates by specifying both the success return shape and the refusal return shape, including all possible refusal reasons. It also covers routing behavior, source scope, and the cost/usage tradeoff against its sibling, so an agent has everything needed to invoke it 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?

The schema has 100% description coverage, with every parameter alias (q, text, input, query, prompt) explicitly mapped to 'question' and a natural-language description. The tool description itself adds little parameter-specific meaning, but the schema fully carries that burden, so a 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 opens with a specific, functional label—'Hallucination-resistant answer mode for high-stakes reads'—and immediately distinguishes this tool from ask_pipeworx by naming it and contrasting their behavior. The verb+resource pairing ('picks the right tool... fetches the data... EXTRACTS the answer') 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 present: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete example domains. It also states the alternative preference directly: 'prefer ask_pipeworx for casual lookups,' giving the agent a clear decision rule.

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