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

The description adds substantial behavioral context beyond the annotations: it reveals the refusal mechanism, enumerates refusal reasons, discloses the extra LLM call cost, and explains that the tool will not invent facts. This goes well beyond the readOnly/idempotent hints already provided.

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 and front-loaded: purpose first, then behavior, then usage guidance and trade-off. Every sentence adds decision-relevant information — including the refusal taxonomy and cost comparison — with no filler or repetition.

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?

For a tool of this complexity with no output schema, the description is remarkably complete: it specifies the success return shape, the refusal shape, all refusal reason enum values, and the exact scenarios for use. The parameter layer is already covered by the schema, so nothing necessary for correct invocation 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%, so the schema already documents the question parameter and its aliases. The description does not need to repeat parameter details; it stays at baseline because it contributes no additional parameter-specific semantics beyond implying that a natural-language question is expected.

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 purpose ('hallucination-resistant answer mode for high-stakes reads') and clearly differentiates from ask_pipeworx by emphasizing extraction grounded only in tool results. It names the exact behavior — returning an answer with evidence or an explicit refusal — so an agent can distinguish it from sibling tools without inspecting them.

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 ('whenever an answer will be quoted, cited, or acted on') with concrete examples. It also gives an exclusion rule ('prefer ask_pipeworx for casual lookups') and names the alternative directly, making routing unambiguous.

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