get_video_frame

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

With no annotations provided, the description discloses key behavioral traits: dependency on yt-dlp and fallback resolution limit (320x180). This gives the agent essential information about possible output quality.

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?

Two short sentences, each packed with critical information. No wasted words; the dependency and fallback are efficiently communicated.

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 is complete enough for a simple screenshot tool: purpose, dependency, fallback. It does not mention return format or error cases, but that is acceptable given the lack of output schema and the tool's straightforward nature.

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 baseline is 3. The description adds meaning to the 'quality' parameter by explaining its default and conditional use (only when yt-dlp is available), improving 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 clearly states the action ('Captures a screenshot from a YouTube video at a specified timestamp') and distinguishes it from siblings like search or transcript tools. The mention of full-quality vs. fallback adds specificity.

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 that yt-dlp is required for full-quality and that fallback occurs without it, providing clear usage context. It does not explicitly state when not to use, but the sibling tools are sufficiently different so confusion is unlikely.

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