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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false, but the description goes far beyond: it details the refusal mechanism with specific refusal_reason values, describes the success return shape including evidence and confidence, and discloses the cost of one extra LLM call. No contradictions 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 information-dense yet efficient. The core purpose is front-loaded in the first sentence, followed by operational details (refusal reasons, return shape) and usage trade-offs. Every sentence earns its place; no filler or redundancy.

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 tool that routes to 5,801 tools and performs extraction, the description covers all essential decision factors: when to use it, what it returns, how it refuses, and its cost relative to the sibling. The output schema is absent, but the description explicitly lists the return fields and refusal cases, making the tool fully usable without needing more detail.

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 description does not add any new parameter semantics beyond what the schema already provides. The schema itself documents 'question' and all aliases, so the description adds no incremental meaning. Baseline of 3 applies because the schema carries the load.

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 precise purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It explains the full pipeline—routing, argument filling, fetching—and the critical extraction step that only uses tool results. It explicitly distinguishes from sibling ask_pipeworx by the extraction and evidence mechanism.

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?

Gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with concrete example domains (financial, legal, medical, public statements). It also tells when NOT to use it: 'prefer ask_pipeworx for casual lookups' and justifies via the extra LLM call cost.

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