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

Goes well beyond the annotations by explaining the refusal behavior, listing all refusal_reason enum values, detailing the return shape, and noting the extra LLM call cost. There is no contradiction with the readOnly, idempotent, or non-destructive annotations; the description adds substantial behavioral context that the annotations alone do not 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 carries essential information: the core behavior, return/refusal format, use cases, and cost/alternative. It is well-structured, front-loaded with the main purpose, and manages to be comprehensive without redundancy.

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?

Despite having no output schema, the description fully specifies the success return object and the refusal structure, including possible reasons. It also places the tool in context: same routing as ask_pipeworx, grounded extraction behavior, cost trade-off, and usage scenarios. This is complete for an agent 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?

The input schema has 100% coverage: every parameter is an alias for 'question' and the canonical parameter is clearly described. The tool description does not need to add parameter semantics because the schema fully documents all six properties. This meets the baseline but adds no extra value beyond the schema.

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 precisely identifies this as a hallucination-resistant, grounded answer mode and contrasts it with ask_pipeworx, its sibling. It clearly states what the tool does conceptually, how it works (routing, fetching, extracting only from tool results), and when this mode matters. This strongly distinguishes it from the sibling tools.

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?

Provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete examples like financial verdicts and legal claims. It also explicitly warns against using it for casual lookups and names ask_pipeworx as the preferred alternative, including the cost trade-off.

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