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

Annotations already declare read-only, idempotent, open-world, and non-destructive behavior, and the description enriches this with the grounded extraction guarantee, explicit refusal reasons, and the extra LLM call cost. It clearly discloses that the tool may return a refusal rather than an answer when evidence is insufficient. No contradiction with annotations exists.

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: it states the behavior, return shape, refusal reasons, use cases, and cost tradeoff. It is front-loaded with the most important defining trait, hallucination resistance, and then provides structured detail. The length is justified by the tool's complexity and high-stakes context.

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 the exact success response fields and the refusal_reason enum values. It also covers routing behavior, cost, and when to prefer the alternative, making it complete for an agent to decide and call correctly. There are no significant gaps in context.

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 coverage is 100% and the schema fully documents the single question parameter and its aliases, so the baseline is 3. The description adds useful context beyond the schema by explaining that the question is routed to an underlying tool that fills arguments from 5,798 tools across 1,517 sources. This clarifies that the user question is high-level rather than a direct function call.

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 states this is a hallucination-resistant answer mode that routes like ask_pipeworx, fetches data, and extracts answers only from tool results. It immediately differentiates itself from ask_pipeworx by emphasizing grounded, evidence-backed answers for high-stakes reads. The verb and resource are specific and the purpose is 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?

The description explicitly says to use this tool whenever an answer will be quoted, cited, or acted on and the agent must not invent facts, with concrete examples like financial verdicts and legal claims. It also names the alternative, ask_pipeworx, and instructs to prefer it for casual lookups. This makes the selection decision unambiguous.

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