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

Beyond the annotations (readOnly, idempotent, openWorld, non-destructive), the description discloses exactly what the tool does with fetched data and what it returns: {answer, evidence, confidence, source, fetched_at, refusal_reason:null} or an explicit refusal with a refusal_reason enum. It also reveals the extra LLM call cost. This is substantial behavioral context that annotations do not provide.

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 front-loaded with the most important distinction and every sentence earns its place: purpose, routing behavior, return/refusal format, when to use, and cost trade-off. Though moderately long, it is dense with actionable information and has no fluff or 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?

Given the tool's complexity and the absence of an output schema, the description is remarkably complete. It explains the return envelope, refusal reasons, when to use it, how it differs from ask_pipeworx, and the extra cost. An agent has everything needed to select and invoke this tool 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?

The input schema covers all 6 parameters at 100% schema description coverage, including the primary 'question' parameter and its aliases. The description does not add much about parameters, but it doesn't need to since the schema already fully describes them. Baseline 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 opens with a specific, distinctive purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly states what the tool does and immediately differentiates it from the sibling tool ask_pipeworx by explaining the same routing but a stricter extraction behavior. The purpose is unambiguous and resource-specific.

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 usage guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with concrete examples. It also names the alternative and when to prefer it: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This leaves no ambiguity about when to select this tool.

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