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

Beyond the annotations (readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false), the description discloses the full return shape, success vs. refusal behavior, and the fact that the answer is extracted strictly from the tool result. It also reveals the extra LLM call cost, which is non-obvious and valuable. 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 thoroughly front-loaded with the core mode, then layers return contract, refusal reasons, usage guidance, and cost. Every sentence adds a distinct fact; there is no filler or repetition, and the structure is easy to scan despite its length.

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?

Even though there is no output schema, the description enumerates both the success object fields (answer, evidence, confidence, source, fetched_at, refusal_reason) and the explicit refusal reasons, plus usage context and cost. An agent has everything needed to invoke and interpret the result 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 description coverage is 100%, and the schema already documents the question parameter and all its aliases (query, q, prompt, text, input). The tool description adds no additional parameter-level detail, which is acceptable because the schema is already self-sufficient. Baseline 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 description states a specific mode: a 'hallucination-resistant answer mode' that extracts answers using only the tool result content. It clearly names the resource (Pipeworx) and differentiates from sibling ask_pipeworx by the extraction behavior and refusal handling, so an agent can distinguish it without opening other tool definitions.

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?

Explicitly states when to use: whenever an answer will be quoted, cited, or acted on and facts must not be invented, with concrete domains (financial, legal, medical, public statements). It also tells the agent to prefer ask_pipeworx for casual lookups and names the cost trade-off, giving clear selection criteria.

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