find_viral_outliers

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

With no annotations, the description carries the full burden of behavioral disclosure. It does explain the detection mechanism (views surpassing the channel baseline by 2.5x/5x/10x+) and the dual query modes, but it never explicitly states that the operation is read-only, mentions authentication requirements, or outlines limitations such as rate limits or data coverage. This is partial transparency, not full.

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 definition is front-loaded with a crisp one-sentence purpose, followed by a useful high-level explanation, and a cleanly formatted Args list. No filler or repetition; every sentence contributes operational detail.

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 moderate complexity, the description covers the core invocation details: what the tool does, what inputs it accepts, defaults, and the outlier logic. An output schema is present, so not describing the return structure is acceptable. A marginal gap is the lack of guidance about what happens when the query matches no creators or topics, but this is a minor omission.

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 0%, so the description must explain each parameter, and it does. The Args section gives semantic meaning for query (niche keyword vs '@creator'), min_multiplier (threshold vs channel benchmark), published_after (RFC 3339 filter), and max_results (candidate count, up to 50). This fully compensates for the empty schema.

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, actionable statement: 'Identify viral outlier videos that perform dramatically above a channel's normal average.' It clearly names the resource (videos), the action (identify/flag), and the distinguishing scope (specific creator or broad niche topic), which separates it from video search tools and channel-level analysis 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 description gives clear context for when to use the tool: discovering breakout video ideas from a creator handle or niche topic. It does not explicitly name alternatives or exclusion criteria, but the input modes and threshold logic imply it is for outlier discovery rather than general search, so agents can make a reasonable usage decision.

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