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

Beyond the readOnly/idempotent annotations, it discloses the exact success return shape, the refusal-reason taxonomy, evidence as a verbatim quote, and the extra LLM call cost. It also states the tool refuses when the data is insufficient rather than fabricating an answer.

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 contributes: mode, mechanism, output contract, refusal behavior, usage guidance, and cost tradeoff. Key behavioral constraints are front-loaded before the longer return details.

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 supplies the full return contract and error/refusal cases. It also provides the decision rule versus ask_pipeworx and notes the performance cost, making the tool self-sufficient for an agent.

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 the schema already defines question and its aliases as natural-language input. The description adds no parameter-level detail beyond that, so the baseline of 3 applies.

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 first sentence defines it as a hallucination-resistant answer mode, and the body explains it selects a tool from 5,743 sources, fetches data, and extracts an answer only from the tool result. This clearly distinguishes it from ask_pipeworx as the grounded variant.

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 explicitly says to use it when an answer will be quoted, cited, or acted on and the agent must not invent facts, lists example high-stakes domains, and instructs to prefer ask_pipeworx for casual lookups. This covers both when and when not to use it.

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