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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, and the description adds substantial behavioral context beyond those: it returns evidence as a verbatim quote, includes confidence and source, and explicitly refuses with a refusal_reason enum when the data does not directly answer. This makes the tool's hallucination-resistant behavior and failure modes transparent.

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 three sentences with high signal density and zero filler. It front-loads the core purpose and usage guidance, then packs the output shape, refusal reasons, cost tradeoff, and sibling preference into the remaining sentences.

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?

Although there is no output schema, the description fully documents the success return shape and the explicit refusal object with its possible refusal_reason values. It also names the closest sibling and the condition for preferring it, making the tool effectively self-contained 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 defines the question parameter plus all aliases in natural language. The tool description adds no parameter-level meaning beyond the general statement that it fills arguments internally, so it correctly relies on the schema rather than duplicating parameter docs.

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 mode—'hallucination-resistant answer mode for high-stakes reads'—and clearly states the action: route to the right source tool, fetch data, and extract an answer using only the tool result. It explicitly distinguishes itself from ask_pipeworx via the grounded, evidence-backed extraction behavior and refusal semantics.

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?

It gives explicit when-to-use guidance: whenever an answer will be quoted, cited, or acted on, especially for financial verdicts, legal claims, medical lookups, and public statements. It also gives the when-not-to-use case by saying to prefer ask_pipeworx for casual lookups, and it names the cost tradeoff of one extra LLM call.

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