resolve-assistant 0.1.4

AI-powered video editing assistant using Gemini + DaVinci Resolve

resolve-assistant

AI-powered video editing assistant that uses Google Gemini to analyze footage, plan professional edits, and build timelines in DaVinci Resolve — automatically. Give it a folder of clips and an editing instruction. It watches every frame, plans the edit, and builds the timeline.

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What is resolve-assistant?

resolve-assistant is an MCP server that turns raw footage into edited timelines using AI. It combines Google Gemini's multimodal video understanding with DaVinci Resolve's professional editing engine.

The workflow is simple:

1. Point it at a folder of clips → Gemini watches every frame, identifies A-roll vs B-roll, transcribes speech, rates quality, tags camera movements, and analyzes music structure
2. Give it an editing instruction → "Cut a 60-second highlight reel with high energy" or "Build a narrative interview piece, lead with the strongest soundbite"
3. It builds the timeline → Gemini plans every cut, selects the best footage, writes a voiceover script, generates director's notes, and constructs the timeline directly in DaVinci Resolve

No manual logging. No spreadsheets. No bin diving. The AI watches your footage and edits it.

Why resolve-assistant?

• AI watches your footage — Gemini processes every video and audio file with frame-level analysis, not just thumbnails or metadata
• Professional editorial decisions — A-roll/B-roll classification, camera movement detection, speech transcription, take quality scoring, filler word detection, music structure analysis
• Complete edit output — timeline built in Resolve + FCP7 XML backup + director's notes + voiceover script + music production brief
• Works with any MCP client — Claude Desktop, Claude Code, Cursor, or any MCP-compatible AI assistant
• Sidecar architecture — analyze once, edit forever. Sidecar JSONs persist next to your media so re-edits are instant
• One-line install — uvx resolve-assistant or pip install resolve-assistant

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Quick Start

Prerequisites

1. Google Gemini API key — free at aistudio.google.com
2. DaVinci Resolve (Free or Studio) running with scripting enabled (Preferences → System → General → External scripting using = Network)
3. Python 3.11+
4. ffmpeg — brew install ffmpeg (macOS) or apt install ffmpeg (Linux)

Install

# Using uvx (recommended — runs in isolated environment)
uvx resolve-assistant

# Using pip
pip install resolve-assistant

# Using pipx
pipx install resolve-assistant

Configure Claude Desktop

Add to your Claude Desktop MCP config:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "resolve-assistant": {
      "command": "uvx",
      "args": ["resolve-assistant"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  }
}

Windows note: If uvx is not on your system PATH, use the full path to uvx.exe (e.g., C:\\Users\\YOU\\.local\\bin\\uvx.exe) as the "command" value.

Configure Claude Code

Add to your project's .mcp.json:

{
  "mcpServers": {
    "resolve-assistant": {
      "command": "uvx",
      "args": ["resolve-assistant"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  }
}

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Using with resolve-mcp — The Complete AI Video Editing Toolkit

resolve-assistant handles AI-driven footage analysis and automatic editing. For direct control over DaVinci Resolve — managing projects, editing timelines, color grading, rendering, Fusion, Fairlight, and 215+ other operations — use resolve-mcp.

They are independent MCP servers that work together. resolve-assistant gives the AI the ability to watch your footage and build edits automatically. resolve-mcp gives the AI hands-on control over every feature in Resolve. Together, they form the most complete AI video editing toolkit available.

Install Both

# Install both with one command
pip install resolve-mcp[assistant]

# Or install separately
pip install resolve-assistant
pip install resolve-mcp

Configure Both Servers Together

Each server needs its own entry in your MCP config. Here is the complete configuration:

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows, ~/.config/Claude/claude_desktop_config.json on Linux):

{
  "mcpServers": {
    "resolve-mcp": {
      "command": "uvx",
      "args": ["resolve-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    },
    "resolve-assistant": {
      "command": "uvx",
      "args": ["resolve-assistant"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  }
}

Claude Code (.mcp.json in your project root):

{
  "mcpServers": {
    "resolve-mcp": {
      "command": "uvx",
      "args": ["resolve-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    },
    "resolve-assistant": {
      "command": "uvx",
      "args": ["resolve-assistant"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  }
}

Note: GEMINI_API_KEY is required for resolve-assistant but optional for resolve-mcp (only its 3 AI bridge tools need it). Both servers use the same key.

Which Server Do I Need?

Use Case	resolve-assistant	resolve-mcp	Both
AI footage analysis — watch every frame, transcribe, tag	Yes
AI automatic editing — instruction to timeline	Yes
Direct Resolve control (projects, timelines, editing, rendering)		Yes
Color grading, Fusion, Fairlight, node graphs		Yes
Full AI video editing pipeline with manual Resolve control			Yes

For more about direct Resolve control, see the resolve-mcp documentation.

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How It Works

Step 1: Ingest — AI Footage Analysis

> "Analyze all clips in /Volumes/Media/Project/Footage"

The ingest pipeline processes every media file:

For video clips:

• Transcodes to a Gemini-safe proxy (HEVC/AAC, ≤1280px) using hardware acceleration (VideoToolbox on Apple Silicon)
• Uploads to Gemini Files API
• Gemini watches the footage at high resolution and produces structured analysis:
  • Segments — timestamped A-roll (speech/interview) and B-roll (visual/cutaway) segments
  • Speech transcription — verbatim transcripts for all spoken content
  • Take quality — good take vs bad take classification for A-roll
  • Filler words — detection of um, uh, like, you know, etc.
  • Camera movement — Pan Left/Right, Tilt Up/Down, Dolly, Truck, Gimbal, Drone, Static, etc.
  • Quality score — 1-10 rating for focus, stability, exposure, and content value
  • Tags — notable objects, locations, actions, and visual elements
• Probes fps and duration with ffprobe (authoritative, never trusts Gemini's guess)
• Saves analysis as a sidecar JSON (e.g., clip001.mov.json) next to the source file

For audio files (music, sound effects):

• Uploads directly to Gemini
• Analysis includes: BPM, musical key, genre, and timestamped sections (intro, verse, chorus, drop, bridge, outro)
• Each section gets energy level (1-10), mood, and instrument tags

Sidecar architecture: analysis results are saved as JSON files alongside your media. Re-running ingest skips already-analyzed files. Delete a sidecar to force re-analysis.

Step 2: Build — AI Edit Planning + Timeline Construction

> "Build a 60-second highlight reel with high energy, cut to the beat"

The build pipeline:

1. Loads all sidecar JSONs from the footage folder
2. Uploads proxy files to Gemini so it can watch the actual footage (not just read metadata)
3. Gemini plans the edit — selects cuts, assigns tracks, sets in/out points, designs camera moves, plans speed ramps, and writes supplementary outputs
4. Builds the timeline in Resolve using AppendToTimeline — clips appear on the timeline with correct source timecodes
5. Renders FCP7 XML backup with proper timecode offsets (probed via ffprobe)
6. Saves supplementary outputs:
   • Edit plan (.edl.json) — complete cut list with all decisions
   • Director's notes (.md) — editorial reasoning for every cut: what was chosen, what was rejected, and why
   • Voiceover script (.txt + .json) — original narration copy timed to the edit, ready for recording or TTS
   • Music production brief (.md + .json) — if no music was provided, a detailed brief for producing an original score: BPM, key, arrangement, instrumentation with ADSR envelopes, lyrics, mix notes, and processing chains

What the AI Editor Does

The AI editor receives the actual footage (not just metadata) and makes professional editorial decisions:

• Clip selection — picks the strongest material from all available footage
• A-roll on V1 — speech, interviews, and primary narrative on track 1
• B-roll on V2 — cutaways, visuals, and supporting footage on track 2
• Music-driven pacing — if audio is provided, cuts land on beats and energy matches section dynamics
• Speed ramps — high-frame-rate clips (≥90fps) automatically get speed ramp control points for dramatic slow-motion
• Camera moves — zoom, pan, tilt, and dynamic zoom ease applied per-clip where they add story value
• Voiceover scripting — original narration written to complement the visuals, timed to fill gaps between A-roll speech
• Director's notes — transparent reasoning for every editorial decision

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MCP Tools

resolve-assistant exposes exactly 4 tools via MCP:

Tool	Description
ingest_footage(folder_path)	Analyze all video and audio files in a folder with Gemini. Runs in the background — returns immediately.
ingest_status(folder_path)	Check progress of a running or completed ingest job. Returns current file, step, and completion count.
build_timeline(folder_path, instruction)	Plan an edit with Gemini and build the timeline in DaVinci Resolve. Uses cached sidecar analysis.
build_status(folder_path)	Check progress of a running or completed build job. Returns status and output paths when done.

Workflow in Practice

You: "Analyze the footage in /Volumes/Media/OffRoad"

Claude: I'll start the ingest process.
→ ingest_footage("/Volumes/Media/OffRoad")
← "Ingestion started for 12 file(s) (10 video using hevc_videotoolbox) (2 audio).
   Use ingest_status('/Volumes/Media/OffRoad') to monitor."

You: "How's it going?"

Claude: Let me check.
→ ingest_status("/Volumes/Media/OffRoad")
← "Ingestion running: 7/12 done. Now analyzing GoPro_0042.mp4."

You: "Build a 30-second social reel — fast cuts, high energy, focus on the jumps"

Claude: I'll start the timeline build.
→ build_timeline("/Volumes/Media/OffRoad", "30-second social reel, fast cuts, high energy, focus on the jumps")
← "Timeline build started (12 sidecars, uploading proxies to Gemini).
   Use build_status('/Volumes/Media/OffRoad') to monitor."

You: "Is it done?"

Claude: Let me check.
→ build_status("/Volumes/Media/OffRoad")
← "Build complete: Timeline 'Off-Road Glory Reel' — 18 cuts.
   TC offsets: 10 probed. Timeline created with 18/18 video clips."

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Sidecar JSON Format

Each analyzed clip gets a sidecar JSON file. Here's what a video sidecar looks like:

{
  "filename": "GoPro_0042.mp4",
  "file_path": "/Volumes/Media/OffRoad/GoPro_0042.mp4",
  "media_type": "video",
  "analysis_model": "gemini-3-flash-preview",
  "fps": 59.94,
  "duration": 47.214,
  "segments": [
    {
      "start_sec": 0.0,
      "end_sec": 8.5,
      "type": "b-roll",
      "description": "Wide establishing shot of desert trail with dust cloud from approaching vehicle",
      "camera_movement": "Static",
      "quality_score": 8,
      "tags": ["desert", "trail", "dust", "wide-shot", "establishing"]
    },
    {
      "start_sec": 8.5,
      "end_sec": 15.2,
      "type": "a-roll",
      "description": "Driver talking to camera: 'This is the gnarliest trail in Moab, we're about to send it'",
      "camera_movement": "Handheld",
      "quality_score": 7,
      "is_good_take": true,
      "filler_words": [],
      "tags": ["interview", "driver", "excitement"]
    }
  ]
}

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Architecture

resolve-assistant/
├── resolve_assistant/
│   ├── __init__.py          # Package init, registers MCP tools
│   ├── __main__.py          # python -m resolve_assistant entry point
│   ├── config.py            # FastMCP server + Gemini client
│   ├── resolve.py           # DaVinci Resolve scripting API connection
│   ├── ingest.py            # Ingest pipeline orchestration
│   ├── ingest_tools.py      # ingest_footage + ingest_status MCP tools
│   ├── ingest_worker.py     # Background ingest worker thread
│   ├── build.py             # Build pipeline orchestration
│   ├── build_tools.py       # build_timeline + build_status MCP tools
│   ├── build_worker.py      # Background build worker thread
│   ├── timeline.py          # OTIO timeline construction + FCP7 XML rendering
│   ├── transcode.py         # Video transcoding for Gemini (VideoToolbox/libx265)
│   ├── media.py             # File discovery + sidecar loading
│   ├── schemas.py           # Pydantic models for sidecar JSON
│   ├── prompts.py           # Gemini prompt templates
│   ├── outputs.py           # Director's notes, voiceover, music brief writers
│   ├── ffprobe.py           # Video metadata extraction
│   ├── resolve_build.py     # AppendToTimeline builder
│   ├── resolve_transforms.py # Clip transform + speed ramp helpers
│   ├── errors.py            # Error handling
│   └── retry.py             # Gemini retry logic with exponential backoff
├── pyproject.toml
├── .env.example
└── README.md

Processing Pipeline

Raw Footage → ffprobe metadata → transcode proxy → Gemini analysis → sidecar JSON
                                                                        ↓
Editing instruction → upload proxies → Gemini edit plan → AppendToTimeline → Resolve timeline
                                            ↓
                                    FCP7 XML + Director's Notes + Voiceover Script + Music Brief

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Transcoding

resolve-assistant automatically transcodes video for Gemini upload:

• Apple Silicon — uses hevc_videotoolbox (hardware HEVC encoder, very fast)
• Other platforms — falls back to libx265 (software encoder)
• Skip criteria — files already in H.264/H.265, under 2GB, and ≤1280px are uploaded as-is
• Cache — transcoded proxies are saved as .gemini.mp4 next to the source and reused on re-ingest
• Resolution cap — 1280px on the longest edge to optimize Gemini token usage while maintaining visual quality

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Troubleshooting

"GEMINI_API_KEY not set"

• Add your Gemini API key to the env section of your MCP config
• Get a free key at aistudio.google.com

"DaVinci Resolve is not running"

• Make sure Resolve is open before starting a build
• Enable scripting: Preferences → System → General → External scripting using = Network
• Note: ingest (analysis) works without Resolve — only build (timeline construction) needs it

"ffmpeg not found"

• Install ffmpeg: brew install ffmpeg (macOS) or apt install ffmpeg (Linux)
• Required for video transcoding before Gemini upload

Ingest is slow

• First run transcodes all videos — this is I/O and CPU intensive
• Subsequent runs skip already-analyzed files (sidecar JSONs exist)
• Hardware encoding (Apple Silicon) is significantly faster than software encoding

Build creates timeline but clips are missing

• Ensure source files are imported into Resolve's media pool
• The builder auto-imports missing files, but the paths must be valid

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Related Projects

• resolve-mcp — The most comprehensive MCP server for DaVinci Resolve. 215+ tools covering projects, media pool, timelines, editing, markers, rendering, color grading, Fusion, Fairlight, galleries, node graphs, Dolby Vision, and stereo 3D. Install alongside resolve-assistant with pip install resolve-mcp[assistant] for the complete AI video editing toolkit.
• FastMCP — The MCP framework powering this server
• Model Context Protocol — The open protocol for AI tool use

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License

MIT License. See LICENSE for details.

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Contributing

Contributions welcome! Please open an issue or PR on GitHub.

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Built for video editors and content creators who want AI to handle the tedious parts of editing — footage logging, clip selection, and rough cut assembly — so they can focus on the creative decisions that matter. Pair with resolve-mcp for 215+ tools giving AI direct control over every feature in DaVinci Resolve.