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get_video_frame

Capture a still frame from any YouTube video at a specified timestamp, returning an image for analysis of on-screen content like slides, captions, or user interfaces.

Instructions

Capture a single still frame (screenshot) from a YouTube video at a moment and return it as an image, so a multimodal model can answer "what's on screen here?".

Use this to see the video at a specific time -- e.g. read a slide, a caption burned into the video, or a UI being demoed. Pair it with get_most_replayed or get_transcript(include_timestamps =True) to pick an interesting moment, then grab the frame there.

Requires ffmpeg on the server. The captured frame is the nearest keyframe at or just before the requested moment (it can be off by a second or two) and is downscaled to max_width to keep the response small.

Args: video: A YouTube URL (watch, youtu.be, shorts, embed, live) or an 11-character video ID. at: The moment to capture -- seconds (e.g. 90) or a "mm:ss" / "h:mm:ss" string. max_width: Max width in pixels of the returned image (clamped 64..1280; default 640). Smaller is cheaper on a vision model's image-token budget.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
videoYes
atYes
max_widthNo
Behavior5/5

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

With no annotations, the description carries full burden and discloses key behaviors: requires ffmpeg on server, captured frame is nearest keyframe (may be off by seconds), downscaled to max_width (clamped 64-1280, default 640), and mentions response size implications. This is thorough for a frame capture tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is front-loaded with purpose, then usage, then technical details and parameters. It is thorough but slightly lengthy; every sentence adds value, so it earns a high score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description does not specify the returned image format (e.g., base64, URL) or error handling. However, it covers parameters well and provides usage context. For a simple tool, it is fairly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but description fully compensates: 'video' explained as YouTube URL or ID; 'at' as seconds or mm:ss format; 'max_width' with range, default, and cost implication for vision models. Each parameter is clearly described beyond schema type.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Capture a single still frame (screenshot) from a YouTube video at a moment and return it as an image'. It specifies the resource (YouTube video) and action, and indirectly distinguishes from sibling tools by mentioning pairing with get_most_replayed or get_transcript.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use this to see the video at a specific time' and provides examples (read a slide, caption, UI). Suggests pairing with get_most_replayed or get_transcript to pick moments, giving clear context for when to use this tool versus siblings.

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

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