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

The description goes well beyond the annotations by detailing the exact return shape, including evidence as a verbatim quote, confidence, source, and refusal reasons. It discloses failure behaviors such as explicit refusals when data does not directly answer, which is valuable for an agent deciding whether to trust the result.

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 first, then output structure, failure modes, usage criteria, and cost tradeoff. It is front-loaded with the most important distinguishing fact and contains no filler.

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 complex grounding tool with no output schema, the description fully compensates by enumerating all success and refusal response fields. It also covers routing behavior, the reason to prefer the sibling tool, and the safety-critical use case, leaving no critical gaps 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 coverage is 100%, so the schema already documents parameter meanings. The description mentions that the tool accepts a natural-language question and routes it to select tools, but it does not add much beyond what the schema already provides for the question parameter.

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 immediately identifies the tool as a hallucination-resistant answer mode and explicitly contrasts it with ask_pipeworx by explaining that it extracts answers only from tool results. It names the exact use case (high-stakes reads) and clearly separates it from sibling tools.

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 gives explicit when-to-use guidance: whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts. It also provides a clear exclusion: prefer ask_pipeworx for casual lookups because this tool costs one extra LLM call.

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