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

Goes well beyond annotations by disclosing the full success and refusal response shapes, the exact refusal reasons, and the behavior of 'using ONLY what the tool result contains.' Also reveals the extra LLM call cost. No contradiction with readOnlyHint, idempotentHint, or destructiveHint; the description enriches them.

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?

Dense without being bloated: it front-loads the core value proposition, then gives routing behavior, return contract, usage guidance, and cost trade-off in logical order. Every sentence contributes operational meaning for the agent.

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 the return envelope and failure modes, making the tool callable and interpretable. It also addresses the key sibling distinction, cost, and high-stakes use cases, leaving no critical gap for correct selection and invocation.

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 documented as aliases for 'question.' The description reinforces that input is a natural-language question but adds no parameter-level semantics beyond what the schema already provides. 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 this as 'Hallucination-resistant answer mode for high-stakes reads' with a specific verb/resource: it routes to tools, fetches data, and extracts grounded answers. It distinguishes itself from sibling ask_pipeworx by emphasizing evidence extraction and refusal behavior, so an agent can tell them apart without opening schemas.

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?

Explicitly states when to use: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It names the alternative (ask_pipeworx) and says to prefer it for casual lookups due to the extra LLM call cost. This is clear, actionable routing guidance.

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