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

Annotations already establish readOnly, idempotent, and non-destructive behavior, and the description adds substantial behavioral context beyond that: explicit success return shape, explicit refusal contract with refusal_reason enum, the 'ONLY what the tool result contains' constraint, and the extra LLM call cost. There is no contradiction with the 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 constraint, return contract, refusal reasons, use cases, and cost comparison with the alternative. It is front-loaded with the most decision-relevant trait, hallucination resistance, and wastes no words despite its length.

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?

Given there is no output schema, the description fully explains the return object and refusal reasons, which is essential for correct invocation and result interpretation. It also covers cost, routing behavior, and usage boundaries, making the definition complete for an agent deciding whether and how to call it.

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 schema already documents the question parameter and all aliases. The description adds no new parameter-level detail, which is acceptable because the schema carries the full burden. Baseline 3 is appropriate.

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 opens with a specific, differentiated purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly defines the tool's job—selecting a routing tool, fetching data, then extracting an answer using only the tool result—and distinguishes it from ask_pipeworx by naming it explicitly. An agent can immediately tell what this tool does and why it exists.

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,' with concrete examples. It also names the alternative and states the tradeoff: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This is model usage guidance.

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