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

Annotations already declare readOnly/openWorld/idempotent, so the bar is lower, but the description goes beyond them by disclosing the exact success and refusal response shapes, the refusal reason enum, the use of verbatim evidence quotes, and the extra LLM call cost. It fully explains what happens when data doesn't directly answer, which is critical behavioral context.

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 long but every sentence earns its place: purpose, routing behavior, output contract, refusal reasons, use cases, and cost tradeoff. It is front-loaded with the core value proposition and structured so an agent can quickly parse the key decision points.

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?

With no output schema present, the description provides a complete success/failure contract including field names, refusal_reason enum values, and the condition triggering refusal. Combined with annotations covering safety and idempotency, nothing needed to call the tool 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?

Schema description coverage is 100% and the description adds no parameter-specific semantics beyond 'question in natural language'. The baseline 3 applies because the schema carries the full burden and the description does not need to compensate.

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 this as a hallucination-resistant, grounded answer mode for high-stakes reads, and immediately distinguishes it from ask_pipeworx by explaining it extracts answers only from tool results and refuses when evidence is insufficient. This gives an agent a precise mental model of 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?

Explicitly states when to use it ('whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts'), gives concrete examples (financial verdicts, legal claims, medical lookups, public statements), and names the alternative to prefer for casual lookups (ask_pipeworx) along with the cost tradeoff. This is exemplary routing guidance.

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