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

Even though annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, the description goes well beyond them: it explains the answer extraction is limited to the tool result, lists the exact success return fields including verbatim evidence, and enumerates the complete refusal_reason enum. It also discloses the extra LLM call cost, which is a meaningful behavioral trait.

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 information-dense without being bloated: it front-loads the core purpose, then details behavior, return shape, usage context, and cost tradeoff in logical order. Every sentence contributes something an agent needs to call the tool correctly or choose between tools.

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 with one real parameter and no output schema, the description is complete: it documents the success return shape, the failure/refusal shape, the sourcing behavior, and the relationship to ask_pipeworx. The annotations cover safety, and the sibling list clarifies alternatives, so nothing critical 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 all six parameters being documented aliases for a single natural-language question, so the schema already fully explains the parameters. The description adds high-level context about high-stakes use but does not need to add parameter-level detail; 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 clearly identifies the tool as a hallucination-resistant answer mode for high-stakes reads, with a specific mechanism: route to the right source, fetch data, and extract the answer only from the tool result. It explicitly differentiates itself from ask_pipeworx by the grounded extraction and refusal behavior, so an agent can distinguish it from its siblings.

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 an explicit when-to-use rule: whenever the 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: prefer ask_pipeworx for casual lookups, citing the extra LLM call cost. This is strong, actionable routing guidance.

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