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

Annotations already indicate read-only, open-world, idempotent behavior. The description adds substantial beyond-annotation context: it will extract only from the tool result, returns a structured success payload, and explicitly refuses with a refusal_reason when data doesn't answer. It also discloses the extra LLM-call cost. 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 dense but every sentence earns its place: purpose, routing, extraction behavior, success/refusal formats, use cases, and cost trade-off. It is front-loaded with the most decision-relevant information and has no 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?

Despite having no output schema, the description fully enumerates the returned fields and all possible refusal reasons, which is sufficient for an agent to interpret results. It also covers cost trade-offs, routing behavior, and usage context, making it complete for a read-only grounded-answer tool.

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 already documents that all six parameters are aliases for the same natural-language question. The description adds no parameter-specific meaning beyond what the schema states, so baseline 3 applies.

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' — and explains what it does: route, fetch, then extract an answer solely from the tool result. It also distinguishes itself from ask_pipeworx by emphasizing strict grounding and explicit refusal, so the agent can tell siblings apart.

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.' It also names the alternative, ask_pipeworx, and says to prefer it for casual lookups because grounded mode costs an extra LLM call. This leaves no ambiguity about selection.

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