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

Beyond the readOnly and non-destructive annotations, the description discloses substantial behavioral traits: refusal reasons (not_in_source, no_tool_match, etc.), the strict 'ONLY what the tool result contains' constraint, and the cost of one extra LLM call. This is rich, accurate context that an agent needs to trust and handle refusal states correctly.

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 model, success/refusal contracts, use cases, and cost trade-off. It is front-loaded with the core purpose and flows logically without repetition. The length is justified by the behavioral complexity of the tool.

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, the description fully specifies both success and refusal return shapes, including the exact refusal_reason enum. It also covers the operational context (cost, alternative tool) and the conditions under which it should be invoked. Nothing an agent needs to call and interpret this 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%, with the question parameter and all its aliases fully documented in the schema. The description adds no parameter-specific meaning beyond mentioning that the tool 'fills arguments' internally, which is routing behavior rather than parameter semantics. Baseline 3 is appropriate because the schema carries the parameter documentation burden.

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 a specific mode: 'Hallucination-resistant answer mode for high-stakes reads' and explains the mechanism: it picks a tool, fetches data, and extracts the answer using ONLY what the tool result contains. It also distinguishes itself from the sibling ask_pipeworx by explicitly contrasting its grounded extraction behavior with the casual-mode alternative.

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 clear when-not-to-use directive: 'prefer ask_pipeworx for casual lookups' because of the additional LLM call cost.

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