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

With no annotations provided, the description carries the burden of behavioral disclosure. It explains the involvement of a vetted human expert, the specific output (verdict, flagged issues, fix suggestions), and the on-chain certificate. It does not mention latency, cost, or asynchronous behavior, but it is quite transparent about the process and results.

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 four sentences, front-loaded with the purpose, followed by usage, output, and certification. Each sentence earns its place with no redundancy or filler. It is well-structured and concise.

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 complexity, the description covers the essential aspects: what it does, when to use it, and what it returns. The output schema exists, so return values are already structured. It lacks information on turnaround time or costs, but for a review tool, it is reasonably complete.

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 100% coverage for both parameters, so the baseline is 3. The description does not add meaningful detail beyond the schema; it mentions 'AI-generated content' but that is not parameter-specific. No additional syntax or format guidance is given.

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 purpose: getting a vetted human expert to review AI-generated content for specific criteria (cultural sensitivity, brand safety, etc.) before publishing. It distinguishes itself from sibling tools like review_content or review_plan by emphasizing the human expert and the prepublish context.

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 the tool: 'Call before social posts, marketing campaigns, content distribution, or anywhere public-facing.' This provides clear context but does not mention specific alternatives or exclusions, so it stops short of a 5.

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