youtube_idea_research_generate_youtube_ideas_action

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

No annotations are provided, so the description bears full responsibility for disclosing behavioral traits. It states the output count (5-7) and mentions 'trending context,' implying external data fetching. However, it does not explain side effects (e.g., whether data is stored), permissions needed, or any cost/rate implications. This minimal disclosure leaves significant behavioral gaps.

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, efficient sentence (12 words) that conveys the core action. It is front-loaded with the verb. However, it is so brief that it sacrifices necessary detail, bordering on under-specification. Still, it earns a 4 for avoiding 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 tool has 4 parameters (2 required, 2 with enums) and no output schema. The description only covers the topic and output count, omitting how parameters shape results, the structure of 'idea cards,' or how 'trending context' is determined. For an agent to use it effectively, more context is needed about expected output and parameter semantics.

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?

Schema description coverage is 25% (only output_variable_name has a description). The tool description adds no meaning for the parameters: it does not explain how 'topic' should be formatted, what 'content_style' or 'content_goal' values mean, or how they influence the output. With low coverage, the description fails to compensate, leaving the agent to rely on parameter names and enums alone.

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 identifies the tool's function: 'Generates 5-7 YouTube video idea cards with trending context for a given topic.' It specifies the verb (generates), resource (YouTube video idea cards), and key feature (trending context). This distinguishes it from sibling render tools like 'youtube_idea_research_render_ideas_report'.

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 implies the tool should be used when a user needs YouTube video ideas with trending context. However, it provides no explicit guidance on when not to use it, nor does it mention alternatives among sibling tools (e.g., other content generation tools). The context is clear but lacks exclusions or comparative direction.

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