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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds substantial behavioral detail beyond the annotations: it only uses the tool result content, returns an explicit refusal with specific refusal_reason values, discloses the extra LLM call cost, and describes both success and failure response shapes. This is exactly the kind of context that helps an agent trust and handle the outcome correctly.

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: the description leads with the core behavioral differentiator, then covers the routing mechanism, return shape, refusal cases, when-to-use guidance, and cost trade-off. It is information-dense but tightly structured, with no filler. The length is justified by the number of critical usage and behavior details.

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 documents the success and refusal response shapes, including the refusal_reason enum values. It also conveys the routing behavior, the source scale, the cost difference, and the intended high-stakes use cases. For a query-answer tool with only one meaningful parameter, this is a complete and self-sufficient definition.

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 documents all parameters with 100% coverage, including the question field and its aliases, so the baseline of 3 applies. The description does not add parameter-level semantic guidance beyond what the schema provides. This is acceptable because the aliases and natural-language intent are already fully captured in 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 states a precise verb and resource: this is a grounded/hallucination-resistant answer mode that routes through the same pipeline as ask_pipeworx but extracts answers only from tool results. It clearly distinguishes itself from the sibling ask_pipeworx by emphasizing the grounded extraction and refusal behavior. An agent can immediately understand what this tool does and which sibling it belongs alongside.

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 says when to use this tool: whenever an answer will be quoted, cited, or acted on, especially for financial verdicts, legal claims, medical lookups, and public statements. It also explicitly states when not to use it by noting it costs one extra LLM call and advising to prefer ask_pipeworx for casual lookups. This gives clear routing guidance versus the primary alternative.

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