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

The description discloses the return structure (answer, evidence, confidence, source, fetched_at, refusal_reason) and the refusal reasons, as well as the extra LLM call cost. This goes beyond the annotations and matches the read-only, open-world, idempotent hints without contradiction.

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 verbose and somewhat repetitive, including details like the tool count (5,801) and source count (1,517) that are not essential for the agent to use the tool. It could be more concise while preserving key information.

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?

The description provides a complete context: it explains the routing, the extraction behavior, the output structure, refusal reasons, when to use, when not to use, and the cost trade-off. This makes the tool's behavior fully understandable without needing an output schema.

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 already fully describes the question parameter and its aliases with 100% coverage. The tool description itself does not add any additional meaning about the parameters, so it remains at the baseline for fully covered schemas.

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 states the tool's purpose: a hallucination-resistant answer mode for high-stakes reads, with explicit extraction from tool results and refusal when no direct answer exists. It also differentiates from ask_pipeworx, making the purpose unambiguous.

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 explicitly states when to use ('whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts') and when to prefer the sibling tool ('prefer ask_pipeworx for casual lookups'). This provides clear, actionable usage guidance.

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