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

The description goes well beyond the readOnly/idempotent annotations by disclosing the exact success and refusal return shapes, listing refusal reasons, and noting the extra LLM call cost. It also explains the grounded extraction behavior, giving agents a realistic model of what can go wrong and what the tool will return.

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 long but information-dense: every clause adds functional value such as routing behavior, extraction constraints, return format, refusal reasons, and cost trade-off. It is front-loaded with the core distinction and avoids filler, though a slightly tighter structure would improve scannability.

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 that there is no output schema, the description thoroughly covers return values, refusal cases, cost implications, and usage context. The tool's safety guarantees, failure modes, and relationship to ask_pipeworx are all specified, leaving no material gap for an agent deciding whether and how to invoke 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 description coverage is 100%, and the parameter is simply a natural-language question with aliases. The description adds no new parameter-level semantics beyond reinforcing that the tool accepts a question, but since the schema already documents this fully, the baseline score 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 the tool as a hallucination-resistant answer mode for high-stakes reads, states the core behavior (retrieves data via shared routing, then extracts answers only from tool results), and explicitly contrasts it with ask_pipeworx. This makes its purpose and differentiation from siblings immediately clear.

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: whenever an answer will be quoted, cited, or acted on and the agent must not invent facts. It also names ask_pipeworx as the cheaper alternative and says to prefer it for casual lookups, providing both positive and negative usage conditions.

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