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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It explains the ranking inputs but does not state what the tool returns, whether output is a ranked list, or any caveats about data recency or sourcing.

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?

A single, front-loaded sentence that communicates the verb, resource, and assessment factors with no filler. Every word contributes to understanding the tool's function.

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?

For a zero-parameter tool, complexity is low, but the lack of an output schema and annotations means the description should specify what the agent will receive. It explains how ranking is done but not the shape or nature of the result.

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 tool has zero parameters, so there is no schema detail needing explanation; a baseline of 4 is appropriate. The ranking criteria in the description add useful semantic color without needing to document individual inputs.

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 uses a specific verb ('Rank') and resource ('YouTube content opportunities') and lists the ranking criteria. This clearly distinguishes it from platform-specific siblings like tiktok_trends or instagram_trends.

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 implied: evaluating YouTube content opportunities. However, there is no explicit when-to-use guidance, exclusions, or named alternatives, so the agent is left to infer selection from the resource name and sibling list.

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