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

The description adds substantial behavioral detail beyond the readOnlyHint/idempotentHint annotations: it explains the exact success return shape, the refusal contract with specific refusal_reason values, and the policy of using only tool result contents. It also discloses the extra LLM call cost. No contradiction with annotations exists.

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: core behavior, routing equivalence, return/refusal shape, usage guidance, and cost trade-off. It is front-loaded with the key hallucination-resistant behavior and avoids fluff.

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 no output schema, the description fully covers return values, failure modes, refusal reasons, and when to choose it over ask_pipeworx. It gives enough context for an agent to invoke it correctly and interpret results safely.

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 all six parameters are documented as aliases for the same natural-language question. The description adds no extra parameter-level meaning, so the baseline of 3 is appropriate; the schema already carries the semantic load.

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 states this is a grounded, hallucination-resistant answer mode for high-stakes reads and explains the mechanism: it routes like ask_pipeworx, fetches data, and extracts answers only from the tool result. It explicitly distinguishes itself from ask_pipeworx by grounding and refusal behavior, so an agent can tell them apart.

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 and the agent must not invent facts, with concrete examples like financial verdicts, legal claims, and medical lookups. It also states when not to use it: prefer ask_pipeworx for casual lookups, because this costs an extra LLM call.

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