Find YouTube outlier videos

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

With no annotations, the description carries full behavioral disclosure. It reveals the tool's cost (~110-130 API units), the logic behind defaults, and that it searches by topic. It does not mention output format or auth requirements, but provides sufficient transparency for typical agent use.

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 long, each with a distinct purpose: purpose + logic, defaults, and cost. It is front-loaded with the most critical information and contains no filler.

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 explains the tool's purpose and usage well, but lacks details about the output format (e.g., fields returned) and potential pagination or sorting. Given the complexity and no output schema, this is a minor gap but still leaves the agent partially uninformed.

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 coverage is 100%, so each parameter is already documented. The description adds little beyond the schema beyond explaining the overall logic and default values (Icon Method). This meets the baseline but does not substantially enrich parameter understanding.

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: search YouTube for outlier videos on small channels that outperform expectations. It uses specific verbs ('Search') and resources ('YouTube videos'), and the logic of format-driven vs. audience-driven success distinguishes it from sibling tools like get_comment_signal or get_video_structure.

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 explains when to use the tool (to find replicable format-driven outliers) and provides clear context (Icon Method criteria). However, it does not explicitly state when not to use it or compare with sibling tools, leaving the agent to infer from context.

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