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

Beyond annotations (readOnly, idempotent, non-destructive), the description discloses the refusal behavior, exact return shape, refusal_reason enum values, and the cost tradeoff of one extra LLM call. This is rich behavioral context that annotations 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 detailed yet efficiently organized: purpose, mechanism, output contract, usage criteria, and cost tradeoff all appear in five purposeful sentences. It is front-loaded with the defining behavior and every sentence 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?

Given the absence of an output schema, the description fully documents the return format and failure modes, including specific refusal reasons. An agent has enough information to decide when to invoke this tool and what to expect from it.

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%, with every parameter described as an alias for the natural-language question. The description adds little about parameter semantics beyond what the schema already covers, but it does reinforce that the input is a natural-language question.

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 behavior: a hallucination-resistant answer mode that routes like ask_pipeworx but extracts answers only from tool results. It clearly distinguishes itself from the sibling ask_pipeworx by emphasizing grounded extraction and explicit refusals.

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: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also names the alternative (ask_pipeworx) and instructs to prefer it for casual lookups, making the selection criterion clear.

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