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

The description goes far beyond the annotations by disclosing the exact success return shape, the refusal reason enum, and the guarantee that answers are extracted only from tool results. It also reveals the cost tradeoff of one extra LLM call. This is valuable behavioral context that annotations alone would 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 dense but every sentence earns its place: purpose, routing behavior, return contract, refusal contract, usage guidance, and cost comparison. The most important purpose and tradeoff are front-loaded, with lower-level refusal details following naturally.

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?

Despite having no output schema, the description fully compensates by specifying both success and refusal response structures. It covers when to use, why it is grounded, the key tradeoff versus ask_pipeworx, and failure modes, leaving no critical gap for correct invocation.

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%, so the baseline is 3, and the schema already documents the question parameter and its aliases. The description adds meaning by explaining that the input drives routing across thousands of sources and automatic argument filling, which helps the agent understand how the question is used.

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 verb and resource: ask Pipeworx in a grounded, hallucination-resistant mode. It explicitly differentiates itself from the sibling ask_pipeworx by describing the extra LLM call and the grounded extraction behavior, so an agent can distinguish it without opening the schema.

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 tells the agent when to use this tool: whenever an answer will be quoted, cited, or acted on and facts must not be invented. It also names the alternative, ask_pipeworx, and explicitly says to prefer it for casual lookups, making both selection and exclusion clear.

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