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

The description discloses important behaviors beyond the annotations: it returns evidence, confidence, source, fetched_at, and structured refusal_reason values; it only uses information contained in the tool result; and it avoids fabrication by refusing when data is insufficient. These details exceed what readOnlyHint/openWorldHint/idempotentHint communicate.

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, success/refusal behavior, use cases, and cost trade-off against the sibling tool are all covered without repetition. The most important distinction is front-loaded.

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 question-answering tool with no output schema, the description provides the full return shape, refusal categories, use-case constraints, and a comparison with the alternate tool. An agent has enough context to select and invoke it correctly.

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%, with the question parameter and all five aliases fully documented in the schema. The description itself does not add parameter-level meaning, which is acceptable because the schema already carries the burden.

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 this as a hallucination-resistant, grounded answer mode that extracts answers strictly from tool results and returns explicit refusals when evidence is insufficient. It also distinguishes itself from the sibling ask_pipeworx by naming the same routing but a stricter extraction 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?

The description explicitly states when to use this tool—when an answer will be quoted, cited, or acted on and facts must not be invented—and when not to use it, saying to prefer ask_pipeworx for casual lookups because this costs an extra LLM call. This is strong, actionable guidance.

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