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

The description reveals extensive behavioral details: it orchestrates 7 deterministic checks, includes an optional LLM deep analysis, explains how PII gates the verdict (SSN/credit card forces BLOCK, email forces FIX), and notes edge cases like regex matching pitfalls. This goes well beyond the annotations and provides high transparency.

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 relatively long but information-dense, covering a complex tool with multiple checks, scoring, and edge cases. It is well-structured with a clear main purpose up front and detailed specifics afterward, avoiding unnecessary 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?

The description gives a good overview of the output (verdict, score, per-check results, recommendations) and covers key behaviors and edge cases. While the output schema is not explicitly shown, the description provides enough context for the caller to understand what to expect. It does not detail every possible scenario but is reasonably complete for the complexity.

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 detailed descriptions for each parameter (e.g., model, prompt, source). The tool description does not add significant new meaning beyond what is already in the schema; it focuses on tool behavior rather than parameter semantics. Since schema coverage is 100%, the baseline is 3.

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 the tool's primary function as a comprehensive AI guardrail analysis on LLM responses, listing the specific checks it orchestrates. It distinguishes itself from individual sibling tools like toxicity_scan or prompt_injection_scan by positioning itself as a unified safety gate.

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 recommends using this as a single-call safety gate before surfacing LLM output, giving a clear usage scenario. However, it does not explicitly contrast it with the individual sibling tools or state when not to use it, so it falls short of perfect explicitness.

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