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

Beyond the readOnlyHint, idempotentHint, and non-destructive annotations, the description discloses meaningful behavioral detail: it uses only the tool result's content, returns evidence as a verbatim quote, and emits explicit refusal reasons such as not_in_source, no_tool_match, tool_error, data_truncated, and llm_error. This gives the agent a clear model of failure modes and safety behavior beyond what structured annotations 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 dense but every sentence earns its place: it states the core behavior, routing, success/refusal return shapes, usage guidance, and cost tradeoff. It avoids repeating schema details and front-loads the most important distinction—hallucination resistance—before moving into supporting detail.

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?

Since there is no output schema, the description fully compensates by specifying the exact success response shape and the refusal response shape with enumerated refusal reasons. It also covers the key decision factor (cost and use case) and the alternative tool, making the description complete 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?

The input schema already documents the single question-like parameter and all its aliases with 100% coverage, so the description does not need to add much parameter detail. The main description adds no new parameter semantics beyond implying that the tool accepts a natural-language question. A score of 3 is appropriate because the schema carries the parameter burden effectively.

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 what the tool does: it is a hallucination-resistant answer mode that routes like ask_pipeworx, fetches data, and extracts answers strictly from the tool result. It also differentiates itself from its sibling by emphasizing the grounded extraction and explicit refusal behavior, so an agent can distinguish it from ask_pipeworx and ask_pipeworx_beta.

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 it whenever an answer will be quoted, cited, or acted on, and when the agent must not invent facts such as financial verdicts, legal claims, medical lookups, or public statements. It also provides the alternative: prefer ask_pipeworx for casual lookups because ask_pipeworx_grounded costs one extra LLM call.

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