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

Beyond the readOnlyHint/openWorldHint/idempotentHint annotations, the description adds valuable behavioral context: it routes through many sources, extracts only from fetched data, returns a structured success shape with verbatim evidence, and explicitly enumerates refusal reasons. The added cost of one extra LLM call is also disclosed. This gives the agent a full behavioral model of the tool.

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?

Every sentence in the description earns its place: what the mode is, how routing works, the success return shape, the refusal contract, when to use it, and the cost tradeoff versus the sibling. The most important qualifier ('Hallucination-resistant answer mode') is front-loaded, and the detail is dense but not redundant.

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?

Despite having no output schema, the description fully compensates by specifying the exact success response fields and the complete refusal_reason enumeration. It also covers routing scale, evidence inclusion, and cost implications. An agent has everything needed to invoke this tool correctly and interpret its result.

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 the natural-language question. The description adds no per-parameter meaning beyond confirming the tool fills arguments and takes a question, so the baseline of 3 applies; 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 names a specific capability—a hallucination-resistant, grounded answer mode—and clearly distinguishes it from the sibling ask_pipeworx by emphasizing extraction strictly from tool results with explicit refusals when data is insufficient. It states both what the tool does and what makes it different, so an agent can select it without ambiguity.

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 provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with high-stakes examples. It also names the alternative and states when to prefer it: 'prefer ask_pipeworx for casual lookups.' This directly answers the route-vs-alternative question.

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