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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses the extra LLM call cost, the explicit refusal behavior with specific refusal_reason values, and the constraint that answers are extracted solely from tool results. This is substantial behavioral context the annotations do not cover.

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?

Every sentence earns its place: purpose, routing, extraction, return format, refusal reasons, usage contexts, and cost trade-off are each conveyed in a compact form. The most critical information is front-loaded.

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 this complexity and no output schema, the description fully covers behavior, return format, refusal scenarios, cost implications, and usage guidance. Nothing an agent needs to invoke it correctly 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?

The schema already documents all six parameters as aliases for 'question' with 100% coverage, so the description adds no new parameter information. The baseline of 3 is appropriate because the schema carries the full burden.

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 clearly identifies the tool as a 'Hallucination-resistant answer mode for high-stakes reads' and explicitly differentiates it from the sibling ask_pipeworx by describing the extraction step and refusal mechanism. The verb and resource are specific, and the sibling contrast leaves no ambiguity.

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 provides explicit usage criteria: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' and directly names the alternative with a preference: 'prefer ask_pipeworx for casual lookups.' This leaves no inference required.

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