Cutible MCP Server
Integrates with OpenAI's API for VLM-based visual analysis of video frames, enabling semantic perception and QC review of rendered content.
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Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Cutible MCP Servermake a 30-second highlight reel from my clips with captions"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Cutible — Agent-Native Montage Engine
A headless video-editing engine whose primary operator is an AI agent, not a human with a mouse. The agent reads the project as data, calls editing verbs, renders deterministically, and inspects the result through a QC loop — then iterates.
Architecture
AGENT-REVISOR (LLM: planning, reasoning, decisions)
│
┌───────────┼───────────┐
│ HANDS │ EYES │ MEMORY
▼ ▼ ▼
Verb API Perception Semantic Media
(14 low + Loop Index
8 high) (VLM+QC) (scenes+transcript+VLM+embeddings)
│ │ │
└─────┬─────┘ │
▼ │
Timeline-as-Data ◄────────┘
(JSON, diffable, auditable)
│
▼
┌─────────────────────┐
│ Deterministic Render │ ← FFmpeg (Contour A)
│ Remotion (Contour B) │ ← Motion graphics
│ Render Farm │ ← Distributed GPU
└─────────────────────┘
│
▼
QC Gate (deterministic + VLM)
│
▼
Final Video / OTIO → DaVinci/PremiereRelated MCP server: DaVinci Resolve MCP Server
What's Implemented
Plan concept | Module | Status |
§4 Timeline-as-Data |
| ✅ pydantic, 3 zooms, content hash |
§3.1 Low-level verbs (14) |
| ✅ diffs, checkpoint/undo/branch |
§3.1 High-level verbs (8) |
| ✅ remove_silences, reframe, beat-sync, captions, ducking, assemble, make_short |
§5 Ingest Pipeline |
| ✅ scenes, Whisper, VLM, audio analysis, embeddings |
§5 Semantic Media Index |
| ✅ models, store, text/time/speaker/B-roll search |
§3.2 Perception Loop |
| ✅ VLM review + proxy render |
§7 Multi-agent Swarm |
| ✅ Planner, Editor, Sound, QC, Orchestrator |
§6.1 Contour A (FFmpeg) |
| ✅ deterministic render |
§6.1 Contour B (Remotion) |
| ✅ TSX generation, config |
§9 OTIO Bridge |
| ✅ export/import to DaVinci/Premiere |
§6.2 Distributed Render Farm |
| ✅ scheduler, workers, assembly |
§8.1 MCP Server |
| ✅ 35 tools, JSON-RPC 2.0/stdio |
§8.2 REST API |
| ✅ FastAPI, full CRUD |
§8.3 Python SDK |
| ✅ in-process + HTTP client |
§8.4 CLI |
| ✅ render/probe/view/qc/ingest/search/agent/export/import/farm |
§12.3 Tests |
| ✅ 30+ tests |
Quick Start
pip install -e . # core
pip install -e ".[api]" # + REST API (FastAPI/uvicorn)
pip install -e ".[whisper]" # + Whisper transcription
pip install -e ".[all]" # everything
# Generate synthetic assets
bash examples/make_assets.sh
# Watch the agent assemble a recap
python examples/agent_recap_demo.pyCLI
# Render
python -m cutible render project.json -o out.mp4 --qc
# Ingest a video into the semantic index
python -m cutible ingest speaker /path/to/speaker.mp4
# Search the index
python -m cutible search "moment where speaker discusses AI"
# Run the multi-agent swarm
python -m cutible agent "make a 60s recap about AI" --duration 60
# Export/Import OTIO
python -m cutible export project.json --otio output.otio
python -m cutible import output.otio --save imported.json
# Distributed render farm
python -m cutible farm project.json -o out.mp4 --workers 4
# Start REST API
python -m cutible serve-api --port 8000Python SDK
from cutible.sdk import CutibleClient
# In-process mode
client = CutibleClient()
client.create_project("demo", fps=30, width=1920, height=1080)
client.add_asset("speaker", "video", uri="speaker.mp4", duration=60)
client.add_track("v_main", "video")
client.add_clip("v_main", "speaker", src_in=0, src_out=10)
result = client.render("output.mp4")
# Run the agent swarm
result = client.run_agent("make a 30s recap", target_duration=30)REST API
# Start server
python -m cutible serve-api
# Create project
curl -X POST http://localhost:8000/projects \
-H "Content-Type: application/json" \
-d '{"id": "demo", "fps": 30}'
# Add clip
curl -X POST http://localhost:8000/projects/demo/verbs \
-H "Content-Type: application/json" \
-d '{"verb": "add_clip", "args": {"track_id": "v1", "asset": "a", "src_out": 5}}'
# Render
curl -X POST http://localhost:8000/projects/demo/render \
-H "Content-Type: application/json" \
-d '{"output": "out.mp4", "run_qc": true}'MCP Server (primary agent interface)
python -m cutible.mcp_server # speaks JSON-RPC 2.0 over stdio35 tools exposed including: create_project, add_clip, trim, split,
ripple_delete, add_transition, add_text_layer, render, qc,
ingest_asset, search_index, remove_silences, reframe_to,
sync_cuts_to_beat, generate_captions, auto_ducking, make_short,
vlm_review, render_proxy, run_agent_swarm, export_otio, import_otio,
render_farm.
Project Layout
cutible/
schema.py Timeline-as-Data models + zoom views + content hash
verbs.py Editor: low-level verbs (14 primitives)
verbs_high.py High-level composite verbs (8 intentions)
compiler.py Timeline → FFmpeg filtergraph → mp4
qc.py Deterministic QC (duration / black frames / LUFS)
cli.py Headless CLI (12 commands)
mcp_server.py MCP stdio server (35 tools)
ingest/
pipeline.py Ingest orchestrator
scenes.py Scene/shot detection (ffmpeg)
audio_transcribe.py Whisper transcription + diarization
vlm.py VLM visual analysis (Gemini/OpenAI)
audio_analysis.py Beat/silence/tempo detection (librosa/ffmpeg)
embeddings.py Embedding generation (CLIP/OpenAI)
index/
models.py Semantic index data models
store.py Index persistence
search.py Text/time/speaker/B-roll search
perception/
vlm_review.py VLM semantic review of renders
proxy_render.py Fast low-res proxy renderer
agents/
base.py Base agent + message types
planner.py Director/Planner agent
editor.py Editor/Montageur agent
sound.py Sound Engineer agent
qc_agent.py QC/Reviewer agent
orchestrator.py Multi-agent swarm coordinator
remotion/
compiler.py Timeline → Remotion (React) project
otio_bridge/
exporter.py Cutible → OpenTimelineIO
importer.py OpenTimelineIO → Cutible
render_farm/
worker.py Segment render worker
scheduler.py Task scheduler
manager.py Distributed render farm manager
api/
app.py FastAPI REST application
sdk/
client.py Python SDK client (in-process + HTTP)
tests/
test_core.py Original 15 tests
test_new_modules.py 20+ tests for new modules
examples/
agent_recap_demo.py End-to-end agent demo
make_assets.sh Synthetic asset generatorDesign Principles (Agent-Native)
State is data, not pixels. The agent reads/diffs/mutates a JSON timeline.
Verbs return diffs. Each call reports what changed.
Errors teach. Structured errors with
hintandcontext.Try / inspect / revert. Checkpoint/undo/branch for exploration.
Deterministic render. Same project → identical frames.
Closed perception loop. QC gate + VLM review → self-correction.
Semantic memory. Ingest → indexed content the agent can search.
Multi-agent swarm. Specialized roles: plan → edit → sound → QC → iterate.
Industry bridge. OTIO export → DaVinci/Premiere for human finishing.
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