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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses the refusal behavior with specific reasons, the guarantee that answers come only from the tool result, the return structure, and the additional LLM call cost. This adds substantial behavioral context not present in the 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 dense but every sentence earns its place: it states the mode's purpose, differentiates it from ask_pipeworx, documents the output and refusal shapes, gives concrete use cases, and provides a cost-based selection guideline. The most important framing ('hallucination-resistant', 'high-stakes') is front-loaded.

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 documents the success and refusal output shapes. It covers routing behavior, grounding guarantees, when to use vs ask_pipeworx, and cost implications. The agent has everything needed to select and invoke the 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 already covers all 6 parameters with aliases, so the high schema description coverage provides a baseline of 3. The tool description does not add parameter-level detail, but it does not need to because the schema already documents the question parameter and aliases completely.

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 ('answer') with a clear resource ('Pipeworx grounded mode'), and explicitly differentiates itself from the sibling ask_pipeworx by describing the grounded extraction behavior. The agent can immediately tell what this tool does and how it differs from the standard ask mode.

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 when-to-use guidance ('use whenever an answer will be quoted, cited, or acted on') and explicit when-not-to-use guidance ('prefer ask_pipeworx for casual lookups'). It also names the alternative tool and highlights the extra LLM call cost trade-off.

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