Short Form Video Editor

Local MCP server that turns a long-form video into short-form (TikTok/Reel) clips by reasoning over a word-timestamped transcript. The agent reads the transcript and designs the edit — including non-contiguous reordering (open on the hook, cut back to the start, build, land the hook again). The server provides accurate STT, a cheap text-space validation loop, a silence-aware renderer, and an STT-based QA gate.

v1 scope: dialog/audio cues only. No smart reframe, source aspect ratio preserved, no burned-in captions. (Those are explicit later phases.)

How it works (the loop)

1. create_project(video_path) — probe + extract 16kHz mono audio.
2. transcribe(project_id) — WhisperX (word timestamps + silence map). Writes transcript.txt / transcript.json.
3. Read transcript.txt and design one or more EDLs (edit decision lists). An EDL is an ordered list of segments, each a word-index range; segments may be reordered/reused.
4. validate_edl(project_id, edl_obj) — snaps cuts to silence, returns the reconstructed dialog in designed order + join warnings. No render. Iterate here cheaply.
5. render(project_id, edl_obj) — ffmpeg cut + concat, one re-encode, frame-accurate.
6. verify_clip(project_id, edl_id) — re-STT the render and diff vs the intended dialog.

EDL shape:

{ "edl_id": "hook-v1", "title": "Whoops it deleted everything",
  "segments": [
    {"from_word": 880, "to_word": 905, "label": "hook"},
    {"from_word": 0,   "to_word": 120, "label": "setup"}
  ] }

v2: reframe, captions & polish (render-layer, all optional on the EDL)

Styling is configured on the EDL and applied by render: cleanup -> cut -> reframe -> captions/title + loudnorm -> multi-aspect.

{ "edl_id":"clip","title":"...","segments":[...],
  "cleanup":   {"remove_fillers": true, "max_pause": 1.0},
  "reframe":   {"mode":"track","aspect":"9:16","zoom":{"hook_punch":true}},
  "captions":  {"enabled": true, "preset":"karaoke-bold"},
  "title_card":{"text":"AI gave itself all the water","hold_s":3},
  "loudnorm":  true,
  "export_aspects": ["9:16","1:1"] }

• reframe mode: track (YOLO11n subject-follow + One-Euro smoothing; center fallback), center, pad (blurred bars), none. Needs a visible person for track.
• captions presets: karaoke-bold (Anton, word-by-word pop), lower-third, minimal-top.
• cleanup: drops filler words + splits at pauses > max_pause.
• The clean cut is always at renders/<edl_id>.mp4 (stable audio for verify_clip); styled deliverables at renders/<edl_id>__<aspect>.mp4.
• New tools: suggest_clips, extract_thumbnail, list_caption_presets, list_reframe_modes.
• New deps: ultralytics, opencv-python. Bundled font: assets/fonts/Anton-Regular.ttf (OFL).

Learn resources (read these first)

The server exposes MCP learn:// resources that bake in the workflow and the lessons: learn://overview, learn://workflow, learn://hooks, learn://cutting, learn://gotchas. An agent should read learn://overview then learn://workflow before driving the tools.

Setup

Requires: ffmpeg/ffprobe on PATH, an NVIDIA GPU (for the default WhisperX large-v3).

# 1. venv (Python 3.11)
py -3.11 -m venv E:\FlowdotPlatform\short-form-editor-mcp\.venv
$py = "E:\FlowdotPlatform\short-form-editor-mcp\.venv\Scripts\python.exe"

# 2. install: this package, CUDA torch, whisperx, (optional) openai
& $py -m pip install --upgrade pip
& $py -m pip install -e E:\FlowdotPlatform\short-form-editor-mcp
& $py -m pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu121
& $py -m pip install whisperx openai

First transcribe downloads the WhisperX model + the wav2vec2 alignment model. Gotcha: depending on the WhisperX/pyannote version, the VAD model may need a one-time Hugging Face token — set HF_TOKEN in the server env if the first run asks for it. We do not use diarization.

Register in .mcp.json

"short-form-editor": {
  "command": "E:\\FlowdotPlatform\\short-form-editor-mcp\\.venv\\Scripts\\python.exe",
  "args": ["-m", "short_form_editor_mcp"],
  "env": {
    "STT_BACKEND": "whisperx",
    "WHISPERX_MODEL": "large-v3",
    "DEVICE": "cuda",
    "WORKSPACE_ROOT": "E:\\FlowdotPlatform\\short-form-editor-mcp\\workspaces",
    "OPENAI_API_KEY": ""
  }
}

Config (env vars)

var	default	meaning
STT_BACKEND	whisperx	whisperx (free, local) or openai (whisper-1; 25 MB file cap)
WHISPERX_MODEL	large-v3	model size
DEVICE	cuda	cuda or cpu
COMPUTE_TYPE	float16/int8	CTranslate2 compute type
MIN_SILENCE	0.15	min gap (s) that counts as a clean cut boundary
SNAP_MARGIN	0.08	how far (s) into the silence to place the cut (capped at gap/2)
CROSSFADE_MS	15	per-join audio fade to kill clicks (0 = off)
WORKSPACE_ROOT	./workspaces	where project data is stored
OPENAI_API_KEY	—	required only for the openai backend
HF_TOKEN	—	only if WhisperX VAD asks for it