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

Annotations already declare the operation read-only, open-world, and idempotent. The description adds substantial behavioral context: a refusal contract with a specific refusal_reason enum, a guarantee to use only tool-result content, and disclosure of an extra LLM call cost. There is no contradiction with 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 front-loaded with the core purpose and every sentence earns its place: routing, extraction guarantee, return shape, refusal reasons, use cases, and cost tradeoff. Despite its length, it remains dense and free of filler.

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?

Even without an output schema, the description fully specifies the success return object and the refusal return object with all possible refusal_reason values. It also covers sibling distinction, usage boundaries, and cost implications, making it complete for correct invocation and result interpretation.

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 input schema covers 100% of the parameters, describing each as an alias for a natural-language 'question'. The description adds no parameter-level information, so the baseline of 3 applies; the schema already carries the semantic load adequately.

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 names a specific mode ('Hallucination-resistant answer mode for high-stakes reads'), states the exact behavior ('EXTRACTS the answer using ONLY what the tool result contains'), and differentiates itself from the sibling 'ask_pipeworx' by name. This leaves no ambiguity about what the tool does.

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?

The description gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts', and provides named alternatives including 'prefer ask_pipeworx for casual lookups' and the cost tradeoff. This fully covers both usage and exclusions.

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