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

Beyond the readOnly/idempotent annotations, the description discloses the grounded extraction mechanism, the exact success and refusal return shapes, and an exhaustive list of refusal_reason values. It also reveals the cost tradeoff of one extra LLM call, which is significant behavioral context not present in 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 yet well-structured, front-loading the core purpose before return contract, usage scenarios, and cost tradeoff. Every sentence adds value; 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 complex tool with no output schema, the description fully specifies the success and failure return contracts, refusal semantics, routing behavior, use cases, and relationship to ask_pipeworx. An agent has everything needed to decide when to invoke it and interpret its result.

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 schema already fully documents all parameters with 100% coverage, including aliases. The description adds context about how the question is processed but no new parameter-level details, so 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 clearly identifies this as a 'hallucination-resistant answer mode' for high-stakes reads, with a specific verb (answer) and resource (Pipeworx). It explicitly contrasts itself with ask_pipeworx by emphasizing grounded extraction and refusal behavior, making it easy to distinguish from siblings.

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 when an answer will be quoted, cited, or acted on and facts must not be invented, citing financial, legal, medical, and public-statement contexts. It also tells the agent to prefer ask_pipeworx for casual lookups, directly addressing the alternative.

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