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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by enumerating supported rule types and stating that it returns pass/fail per rule, which is useful beyond the annotations.

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 a single sentence that front-loads the core purpose, lists rule examples for clarity, and ends with the return behavior. Every word earns its place; there is no redundancy or fluff.

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 the tool's modest complexity (two parameters) and the presence of an output schema, the description is adequately complete. It mentions the return format (pass/fail per rule) and enumerates rule types, though it does not address edge cases like invalid regex, which could be inferred from the schema.

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 provides descriptions for both parameters, with 100% coverage. The description's list of rule types echoes the enum values in the schema without adding new semantic detail, so it contributes little beyond the structured documentation.

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 states what the tool does: tests an LLM response against guardrail rules, with a specific verb and resource. It lists the rule types, which distinguishes it from sibling tools like toxicity_scan or prompt_injection_scan that focus on specific safety dimensions.

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 establishes a clear context for use: validating LLM responses against custom guardrail rules. It does not explicitly name alternatives or exclusion criteria, but the specific rule categories imply a general-purpose guardrail testing role distinct from more specialized sibling tools.

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