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

Beyond the readOnly/idempotent/openWorld annotations, the description discloses the internal mechanism, the refusal policy with specific reason codes, and the exact success and refusal return shapes. It also adds the practical cost signal of one extra LLM call, which is material for tool selection.

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 is front-loaded, the mechanism is summarized efficiently, return/refusal shapes are given, and the use-case and cost tradeoff close the definition without repetition.

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?

With no output schema present, the description fully compensates by specifying both success and refusal response structures. It also explains when to choose this tool over ask_pipeworx and what happens when the source data cannot answer, making the tool self-contained 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?

The input schema already provides 100% coverage, documenting the required question parameter and five aliases. The description adds domain context but no new parameter-level semantics, so the baseline of 3 applies.

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 names a specific verb and resource: it is a hallucination-resistant answer mode that routes through the same tool-selection machinery as ask_pipeworx and then extracts an answer strictly from the fetched tool result. This directly distinguishes it from ask_pipeworx and other siblings by emphasizing groundedness and explicit refusal behavior.

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 explicitly states when to use this tool: any time the answer will be quoted, cited, or acted on and facts must not be invented. It also names ask_pipeworx as the lighter alternative for casual lookups, citing the extra LLM call cost tradeoff.

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