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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, but the description goes well beyond them by detailing the exact success return shape, the explicit refusal reasons, the fact that answers are extracted only from tool results, and the additional cost. This gives the agent a precise behavioral contract.

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 rule, return contract, use cases, and cost tradeoff. It is dense but well-organized, with the most important distinguishing detail ('grounded', 'extracts from tool result') presented first.

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 documents return values and refusal reasons. It also addresses the operational context (extra LLM call, when to use vs sibling), making it complete for an agent deciding to call and interpret the 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?

The schema already covers 100% of parameters, all of which are aliases for the single 'question' string. The description does not add new parameter-specific semantics, but the schema is sufficient on its own, so the baseline of 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 clearly identifies this as a hallucination-resistant answer mode for high-stakes reads, with a specific extraction mechanism and refusal behavior. It explicitly distinguishes itself from ask_pipeworx by noting same routing but grounded extraction, which differentiates it from its closest sibling.

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?

Provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete examples. It also gives a when-not-to-use rule by recommending ask_pipeworx for casual lookups and notes the extra LLM call cost as a tradeoff.

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