calculate_engagement

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

With no annotations, the description carries the behavioral disclosure burden. It states the calculation basis ('based on view count') and the metric types, which is helpful. However, it does not describe the return format, units, possible failure modes for invalid/private videos, or whether it fetches live data.

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?

One sentence, zero filler, and the essential output information is front-loaded immediately after the verb. The colon-separated metric list is compact and scannable.

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 one-parameter, read-only calculation tool, the description is almost complete: it names the input and the expected outputs. It lacks a precise return structure or unit specification (e.g., decimal vs. percentage), but this is a minor gap given the tool's simplicity.

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 100%, so the schema already documents the single parameter as 'YouTube video URL or video ID'. The tool description adds conceptual context about view counts but does not meaningfully enrich the parameter semantics beyond the 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 uses a specific verb ('calculate') with a clear resource ('engagement metrics for a YouTube video') and enumerates the exact outputs: like rate, comment rate, and overall engagement rate. This clearly distinguishes it from all sibling tools, none of which focus on engagement calculations.

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: use this tool when engagement metrics for a YouTube video are needed. It does not explicitly name alternatives or exclusions, but among the sibling tools there is no competing engagement-calculator, so the intended use is unambiguous.

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