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

Beyond the readOnly and idempotent annotations, the description discloses refusal behavior with an exact contract: success returns evidence and confidence, and failure returns one of five explicit refusal_reason values when data doesn't directly answer. It also states that it costs one extra LLM call, which annotates unobservable cost behavior.

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 purpose and exception flow remains dense throughout. The source-count detail (5,724 tools across 1,497 sources) is not essential for invocation, but the rest of the content — routing, return shape, refusal reasons, use cases, and cost tradeoff — earns its place.

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?

With no output schema present, the description fully specifies the success and failure return contracts, including the refusal_reason enum. It also covers the routing behavior, when to use it, and the cost tradeoff, so an agent has everything needed to invoke and interpret 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?

Schema description coverage is 100% and every parameter is already described as a natural-language question or alias. The tool description adds no parameter-level meaning beyond that, so the schema carries the burden and the baseline 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 opens with a specific, differentiating purpose: hallucination-resistant answer mode for high-stakes reads. It distinguishes itself from ask_pipeworx by explaining it routes the same way but then extracts the answer using only the tool result, so an agent can tell them apart.

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?

It gives explicit when-to-use guidance: whenever the answer will be quoted, cited, or acted on and the agent must not invent facts, with concrete domains. It also tells the agent to prefer ask_pipeworx for casual lookups, naming the alternative and the condition that selects it.

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