zuckerbot_research_reviews

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

No annotations are provided, so the description must bear the full burden of behavioral disclosure. It describes what the tool does (searches Google/Yelp, returns structured data), but lacks details on limits, authentication requirements, or response format. It is adequate but not thorough.

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 three sentences with clear front-loading: purpose, output detail, and usage recommendation. No redundant or unnecessary information.

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?

Although no output schema exists, the description adequately outlines what the tool returns (star rating, review count, sentiment themes, quotes). Given the tool's simplicity (3 parameters, simple search) and the presence of sibling tools, it provides sufficient context for an AI agent to decide when and how to use it.

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 covers 100% of parameters with descriptions. The tool description does not add additional semantic value beyond the schema's existing documentation for 'business_name', 'location', and 'platform'. Baseline of 3 is appropriate.

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 fetches review intelligence for a business by name, specifying sources (Google, Yelp) and outputs (star rating, review count, sentiment themes, quotes). This distinguishes it from sibling research tools like zuckerbot_research_competitors or zuckerbot_research_market.

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 advises using it 'before creating a campaign' to identify proof points and objection-handling angles. While it doesn't explicitly exclude alternatives, the use case is clearly contextualized, and no competing tool performs the same function.

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