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

Beyond the annotations, the description discloses the exact return shape on success including evidence, confidence, source, and fetched_at, plus the full refusal contract with all possible refusal_reason values. It also surfaces an extra behavioral cost — one additional LLM call — which is important for an agent deciding between this and ask_pipeworx. This significantly exceeds what readOnlyHint/openWorldHint already declare.

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 largely efficient, leading with purpose, then returning the result contract, then usage guidance, and then cost tradeoff. Some details like '5,908 across 1540 sources' add credibility but are not strictly necessary, and the routing sentence is slightly repetitive given that ask_pipeworx is already named.

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 high-stakes grounded-answer tool with no output schema, the description is thorough: it explains what the tool does, how it differs from the sibling, what success and refusal responses look like, when to use it, and when not to. An agent has everything needed to select and invoke it correctly.

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% and the schema already documents the single meaningful parameter, question, including its aliases. The description says the tool 'fills arguments' during routing but adds no parameter-specific semantics beyond what the schema provides, so the baseline score of 3 is appropriate.

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 identifies this as a hallucination-resistant answer mode for grounded question answering, with a specific extraction behavior: answers are drawn only from tool results. It distinguishes itself from ask_pipeworx by emphasizing evidence-backed output and refusal behavior, so an agent can tell it apart from siblings without opening schemas.

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 explicitly states when to use this tool: whenever an answer will be quoted, cited, or acted on, and facts must not be invented, with examples like financial verdicts, legal claims, and medical lookups. It also gives a clear exclusion: prefer ask_pipeworx for casual lookups due to the extra LLM call cost. This is explicit and actionable.

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