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

This goes well beyond the annotations. It discloses the exact success return shape, the refusal envelope with specific refusal_reason values, and the condition under which refusals occur. It also transparently notes the extra LLM call cost. These are meaningful behavioral traits that annotations alone 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 dense but every sentence earns its place: purpose, routing behavior, return/refusal formats, usage guidance, and cost tradeoff. Key differentiators are front-loaded, and the structure makes the tool's behavior easy to parse.

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 lacking an output schema, the description fully explains what the tool returns on success and failure, including refusal reasons. It also provides enough context for invoking the tool with a single required question parameter and correctly choosing it over siblings. Nothing essential for safe and correct use is missing.

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 schema clearly documents the single meaningful parameter 'question' along with all aliases. The description itself adds little about parameter semantics, but because the schema already carries that burden, the 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 purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly identifies the resource (Pipeworx tool routing) and the distinguishing behavior (extracts answers using ONLY tool result content). It also contrasts with the sibling ask_pipeworx, so an agent can distinguish them immediately.

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 with 'Use whenever an answer will be quoted, cited, or acted on' and names concrete domains where hallucination avoidance matters. It also provides an explicit alternative: 'prefer ask_pipeworx for casual lookups,' including the cost tradeoff of one extra LLM call. This fully covers when, when-not, and alternatives.

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