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

Beyond the annotations (read-only, open-world, idempotent, non-destructive), the description discloses the full behavioral contract: success returns {answer, evidence, confidence, source, fetched_at, refusal_reason:null}, and failure returns an explicit refusal with enumerated reasons. It also reveals the extra LLM call cost and the strict 'only what the tool result contains' extraction policy. No contradiction with annotations exists.

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 carries distinct value: the mode's purpose, the routing behavior, the success and refusal payloads, the use cases, and the cost tradeoff. Key information is front-loaded ('Hallucination-resistant answer mode for high-stakes reads') and the rest unfolds logically without repetition.

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?

This is a complex tool with no output schema, so the description must carry the return-format burden—and it does, specifying both success and refusal shapes. It also covers when to use it, when not to, and how it differs from its sibling. Nothing critical is missing for an agent to 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 describes the single logical parameter question and its five aliases. The description adds context that the question is used for routing and argument-filling, but it does not add syntax, formatting, or per-parameter detail. With complete schema coverage, this is the appropriate baseline.

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 names the tool as a 'Hallucination-resistant answer mode' for Pipeworx, and then details the behavior: route to the right tool, fill arguments, fetch data, and extract an answer using only the tool result. It explicitly distinguishes itself from its sibling ask_pipeworx by the grounded, evidence-based extraction and refusal behavior, so an agent can tell them apart without inspecting 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 gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' followed by concrete domains. It also states the tradeoff ('Costs one extra LLM call vs ask_pipeworx') and tells the agent to prefer ask_pipeworx for casual lookups, which is exactly the kind of when-to-use-vs-alternative guidance an agent needs.

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