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

Annotations cover read-only, open-world, and idempotent behavior, and the description adds substantial context beyond that: refusal reasons, the structured response shape, the guarantee that answers derive only from tool results, and the cost tradeoff of one extra LLM call. No contradiction with annotations.

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 longer than average but every segment carries information: behavior, routing, return contract, refusal reasons, use cases, and cost. It is front-loaded with the most important distinguishing phrase and does not waste words, though the tool-source count detail ('5,767 across 1506 sources') is slightly extraneous.

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?

Given the tool's complexity — routing, extraction, refusal states, and tradeoffs against a sibling — the description is complete. An agent can decide when to invoke it, what to expect on success and failure, and how it differs from ask_pipeworx without any information beyond this text and the provided schema/annotations.

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 documentation covers 100% of parameters, so the baseline is 3. The description mentions that the tool fills arguments automatically and accepts a natural-language question, but it does not need to add parameter-level detail since the schema already describes the question parameter and its aliases.

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, meaningful label — 'Hallucination-resistant answer mode for high-stakes reads' — and states the exact behavior: extracts answers using only tool result contents. It explicitly contrasts with ask_pipeworx ('Same routing as ask_pipeworx'), making the distinction between siblings clear.

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: when the answer will be quoted, cited, or acted on, and when facts must not be invented (financial verdicts, legal claims, medical lookups, public statements). It also names the alternative and the condition for preferring it: 'prefer ask_pipeworx for casual lookups.'

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