mcp-video-analyzer

Analyze a video URL to extract transcript, key frames, metadata, comments, OCR text, and annotated timeline.

Returns structured data about the video content:

• Transcript with timestamps and speakers

• Key frames extracted via scene-change detection (deduplicated, as images)

• OCR text extracted from frames (code, error messages, UI text visible on screen)

• Annotated timeline merging transcript + frames + OCR into a unified chronological view

• Metadata (title, duration, platform)

• Comments from viewers (if available)

Supports: Loom (loom.com/share/...) and direct video URLs (.mp4, .webm, .mov).

Detail levels:

• "brief": metadata + truncated transcript only (fast, no video download)

• "standard": full analysis with scene-change frames (default)

• "detailed": dense sampling (1 frame/sec), more frames, full OCR

Use options.fields to request only specific data (e.g., ["metadata", "transcript"]). Use options.forceRefresh to bypass the cache.

Extract a single video frame at a specific timestamp.

Useful for inspecting what's on screen at a particular moment. The AI reads the transcript, identifies a critical moment, and requests the exact frame at that timestamp.

Supports: Loom (loom.com/share/...) and direct video URLs (.mp4, .webm, .mov). Requires video download capability — direct URLs work best.

Args:

• url: Video URL

• timestamp: Time position (e.g., "1:23", "0:05", "01:23:45")

Returns: A single image of the video frame at the specified timestamp.

Extract multiple frames evenly distributed across a time range.

Designed for motion and vibration analysis where scene-change detection fails because the "scene" doesn't change — only the position/state of objects does.

Example: get_frame_burst(url, "0:15", "0:17", 10) → 10 frames in 2 seconds

• AI sees the object in different positions across frames → understands the vibration

• Works for: shaking, flickering, animations, fast scrolling, loading spinners

Supports: Loom (loom.com/share/...) and direct video URLs (.mp4, .webm, .mov). Requires video download capability — direct URLs work best.

Args:

• url: Video URL

• from: Start timestamp (e.g., "0:15")

• to: End timestamp (e.g., "0:17")

• count: Number of frames (default: 5, max: 30)

Returns: N images evenly distributed between the from and to timestamps.

Extract only the transcript from a video URL.

Returns timestamped transcript entries with speaker identification (when available). Faster than analyze_video when you only need the transcript.

If the platform has no native transcript, attempts Whisper fallback transcription (requires @huggingface/transformers, whisper CLI, or OPENAI_API_KEY).

Supports: Loom (loom.com/share/...) and direct video URLs (.mp4, .webm, .mov).

Get video metadata, comments, chapters, and AI summary from a video URL.

Returns structured metadata without downloading the video or extracting frames. Faster than analyze_video when you only need metadata.

Supports: Loom (loom.com/share/...) and direct video URLs (.mp4, .webm, .mov).

Extract key frames from a video URL without transcript or metadata.

Two extraction modes:

• Scene-change detection (default): captures visual transitions

• Dense sampling (dense=true): captures 1 frame/sec for full video coverage

Returns optimized, deduplicated JPEG frames.

Supports: Loom (loom.com/share/...) and direct video URLs (.mp4, .webm, .mov).

Deep-dive analysis of a specific time range in a video.

Combines burst frame extraction + transcript filtering + OCR + annotated timeline for a focused segment of the video.

Use this when you need to understand exactly what happens between two timestamps:

• What's on screen (frames + OCR text extraction)

• What's being said (transcript filtered to the range)

• Unified timeline merging visual and audio content

Example: analyze_moment(url, "1:30", "2:00", 10) → 10 frames + transcript + OCR for that 30s window

Supports: Loom (loom.com/share/...) and direct video URLs (.mp4, .webm, .mov). Requires video download capability for frame extraction.