find_cross_language_opportunities

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

With no annotations provided, the description carries the transparency burden and it does well by explaining that it analyzes viral performance, checks competition levels, and produces translated title frameworks and arbitrage scores. These verbs convey a read-only research operation, and the scope of languages/regions is disclosed.

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 front-loaded with a crisp summary, followed by a valuable process detail sentence and a clearly structured Args block. Every sentence contributes information and there is no filler or redundancy.

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?

All parameters are covered, examples and defaults are given, and an output schema exists to document the return shape. It lacks explicit guidance on language/region pairing or error behavior, but these are minor gaps for this kind of research tool.

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 provides 0% description coverage, so the Args section fully compensates by explaining every parameter with concrete examples, language and region code lists, and a default for max_results. Without this section, the agent would only see parameter names and types.

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 opens with a specific verb and resource: 'Identify proven viral US/English video concepts with low competition in non-English markets.' It further clarifies the analytic behavior, target locales, and outputs such as translated title frameworks and market arbitrage scores, making it easily distinguishable from sibling research tools.

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 intended use case is clear: find opportunities to adapt successful English videos into non-English markets by comparing viral performance and competition. It does not explicitly name alternatives or state when not to use it, but the scenario is unmistakable from the description.

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