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

The description goes far beyond the annotations by disclosing the routing pipeline, the extraction constraint (ONLY from tool result), the exact deterministic success object, and the full refusal reason enum. None of this is visible in the annotations or schema, so it substantially improves transparency. The annotations readOnlyHint/openWorldHint/idempotentHint are consistent with the described read-only, grounded behavior.

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?

Every sentence earns its place: it front-loads the purpose, explains the process, enumerates exact success/refusal outputs, and closes with a cost-based routing rule. Though dense, the length is justified because the tool is high-stakes and needs to be understood before invocation.

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 tool with no output schema, the description thoroughly explains the return shape and refusal behavior, which is exactly what an agent needs. It also covers routing behavior, when to prefer it, and the cost tradeoff, making the entire call contract self-sufficient.

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 provides 100% coverage of the parameters, including the natural-language question and its five aliases. The description does not add parameter-level detail, but given the aliases make all parameters equivalent, no extra parameter semantics are truly necessary. It earns the high-coverage baseline.

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 the tool as a hallucination-resistant, grounded answer mode and separates it from its sibling ask_pipeworx by explaining that it extracts answers using only tool-result content. It names the exact high-stakes use case and explicitly contrasts itself with the casual-lookup sibling.

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 — quoted, cited, or acted-on answers where inventing facts is unacceptable — and when not to use it: casual lookups, for which ask_pipeworx is preferred. It also discloses the cost tradeoff of one extra LLM call, giving the agent clear, actionable routing criteria.

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