videolab-mcp
An MCP server that turns Claude into a hands-on video editor for short-form videos, enabling music generation, script writing, voiceover synthesis, and video stitching with FFmpeg. It also features a text-to-documentary skill that converts long-form text into structured documentary videos.
README
videolab-mcp
An MCP server that turns Claude (or any MCP host) into a hands-on video editor for short-form videos. Browse a music library or generate new tracks. Animate stills with Veo or lip-sync portraits with OmniHuman. Write and rewrite scripts. Synthesize voiceover with ElevenLabs. Stitch the timeline with FFmpeg, play the result, then iterate cheaply by swapping the music, voiceover, or any single clip — without re-rendering from scratch.
It also ships a text-to-documentary skill that converts long-form text (PDFs, books, papers) into structured documentary videos with AI-generated voiceover, captions, and b-roll.
Standalone, portable, configurable. Bring your own API keys.
What's in the box
Tools across the workflow:
| Category | Tools |
|---|---|
| Music | list_music, preview_music, generate_music |
| B-roll | list_broll, preview_broll |
| Scene assets | list_scene_assets, get_scene_asset |
| Images | generate_image, list_images, get_image |
| Voiceover (ElevenLabs) | list_voices, generate_voiceover, preview_voiceover, list_voiceovers |
| Script (Anthropic) | generate_script, rewrite_script, get_script, list_scripts |
| Video stitching (FFmpeg) | assemble_promo, swap_music, swap_voiceover, swap_clip, play_render, list_recent_renders, describe_render |
| Animation (Veo) | animate_image_to_video |
| Talking heads (Fal OmniHuman) | generate_talking_head |
| Documentary | extract_pdf, split_chapters, plan_documentary_scenes, validate_attention |
| Diagnostics | ping, describe_capabilities |
Resources for browsing without burning tool calls: library://music, library://broll, library://renders, library://voiceovers, library://scripts, library://scenes.
Prompts for guided multi-step flows: make-scene-promo, remix-render, compose-music-for-scene.
Skills in skills/: text-to-documentary — PDF/book → chaptered documentary videos with structured narrative arcs.
Requirements
- Node.js 18+
- FFmpeg on your PATH (or set
ffmpeg.binaryto an absolute path in the config) - API keys for whichever providers you use (none are required upfront — lazy-init only on first call)
Quickstart
git clone <this-repo> videolab-mcp
cd videolab-mcp
npm install
npm run build
cp videolab.config.example.json videolab.config.json
cp .env.example .env # then fill in keys you have
Wire it into Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %AppData%\Claude\claude_desktop_config.json on Windows):
{
"mcpServers": {
"videolab": {
"command": "node",
"args": ["C:\\path\\to\\videolab-mcp\\build\\index.js"],
"env": {
"ELEVENLABS_API_KEY": "...",
"ANTHROPIC_API_KEY": "...",
"GOOGLE_VEO_API_KEY": "...",
"FAL_KEY": "...",
"VIDEOLAB_CONFIG": "C:\\path\\to\\videolab-mcp\\videolab.config.json"
}
}
}
}
Restart Claude Desktop. Open a chat and call ping to confirm the connection.
Configuration
Two files. .env for secrets only:
ELEVENLABS_API_KEY=
ANTHROPIC_API_KEY=
GOOGLE_VEO_API_KEY=
FAL_KEY=
videolab.config.json for everything else (storage, providers, FFmpeg flags, defaults). The example file is sensible and zero-dependency: local storage, JSON index, ElevenLabs/Anthropic/Veo/Fal providers, mobile (9:16) defaults.
One required setting: defaults.voiceId — your ElevenLabs voice id. Run list_voices once the server is up (or grab the id from the ElevenLabs dashboard) and paste it into your config. Validation will fail until this is set.
Walkthrough — make a promo from scratch
In Claude Desktop, with the MCP server connected:
User: Make a 20-second mobile promo for the test scene.
Claude:
→ list_scene_assets({ sceneRef: { key: "test-scene" } })
→ 3 assets: magenta, cyan, orange
→ generate_script({
context: "Test scene with three coloured backdrops",
durationSeconds: 20, tone: "punchy"
})
→ scriptId: script_a3f, slot breakdown shown
→ generate_voiceover({ text: "<the script>" })
→ voiceoverId: vo_8d2
→ generate_music({
prompt: "driving electronic, slow build, retro synth pad",
durationMs: 20000
})
→ trackId: music_4e1
→ assemble_promo({
timeline: {
platform: "mobile",
slots: [
{ source: { kind: "scene", id: "asset-test-magenta" }, durationMs: 7000 },
{ source: { kind: "scene", id: "asset-test-cyan" }, durationMs: 7000 },
{ source: { kind: "scene", id: "asset-test-orange" }, durationMs: 6000 }
]
},
voiceoverId: "vo_8d2",
musicId: "music_4e1"
})
→ renderId: render_94c, autoplays in default video player
User: Make the music more chill.
Claude:
→ generate_music({ prompt: "soft ambient pad, gentle rhythm", durationMs: 20000 })
→ trackId: music_c70
→ swap_music({ renderId: "render_94c", newMusicId: "music_c70" })
→ renderId: render_d11 (rev of render_94c) — only re-mixes audio (~2s)
User: Replace the orange shot with a Veo animation of the magenta image zooming in.
Claude:
→ animate_image_to_video({
imageSource: { kind: "scene", id: "asset-test-magenta" },
prompt: "slow camera push-in, dust particles drifting"
})
→ clipId: broll_veo_a8b
→ swap_clip({
renderId: "render_d11",
slotIndex: 2,
newSource: { kind: "broll", id: "broll_veo_a8b" }
})
→ renderId: render_2f9 (rev of render_d11)
The iteration loop
This is the part that makes the workflow feel good:
assemble_promowrites per-slot intermediates (slot_*.mp4), a silentvisuals.mp4, the audio mix, and the finaloutput.mp4— all undermedia/renders/<renderId>/.swap_music/swap_voiceoverreuse the parent'svisuals.mp4and only re-mix audio. Typical wall time: ~2 seconds.swap_cliprebuilds the visuals stream + remixes audio. Typical wall time: ~5–10 seconds.- Every render is a new
renderIdlinked viaparentId— you never lose an earlier version.
Text-to-documentary mode
The skills/text-to-documentary/ skill turns a PDF, book, or pasted long-form text into a series of ~5-minute documentary videos — one per chapter. Each video has a structured narrative arc (Hook → CoreIdea → Examples → PatternInterrupts → MicroRecaps → Cliffhanger), karaoke captions from ElevenLabs alignment timestamps, and AI-generated b-roll.
Tools used: extract_pdf, split_chapters, plan_documentary_scenes, validate_attention, generate_voiceover, generate_image, animate_image_to_video, generate_music, assemble_promo.
If you're using Claude Code or another host that supports skills, the skill auto-loads when triggered ("turn this PDF into a documentary", "make videos from this book", etc.). Otherwise read skills/text-to-documentary/SKILL.md for the full step list and call the tools directly.
Custom scene-asset provider
The server is provider-agnostic for scene assets. The shipped json-manifest provider reads from a JSON file. Anything more elaborate (your CMS, a database, an API) gets implemented as a SceneAssetProvider:
export interface SceneAssetProvider {
readonly kind: string;
describeRefShape(): string; // shows up in the tool description so the host knows what to send
listAssets(ref: SceneRef): Promise<SceneAsset[]>;
getAsset(id: string): Promise<SceneAsset | null>;
}
Drop your implementation into src/providers/scene-assets/<your-name>.ts, register it in src/providers/factory.ts under buildSceneAssets, and add it to the config schema in src/config.ts. Same pattern works for storage backends (S3, Azure) — see src/providers/types.ts:StorageProvider.
Provider matrix
| What | Default provider | Env var | Config field |
|---|---|---|---|
| Storage | local | — | storage.kind |
| Index (asset metadata) | json | — | index.kind |
| Music generation | ElevenLabs Music | ELEVENLABS_API_KEY |
providers.musicGen |
| TTS | ElevenLabs | ELEVENLABS_API_KEY |
providers.tts |
| Script LLM | Anthropic Claude | ANTHROPIC_API_KEY |
providers.llm |
| Image generation | Gemini Nano Banana | GOOGLE_VEO_API_KEY |
providers.imageGen |
| Image-to-video | Google Veo | GOOGLE_VEO_API_KEY |
providers.animate |
| Talking-head | Fal OmniHuman | FAL_KEY |
providers.talkingHead |
| Scene assets | json-manifest | — | providers.sceneAssets |
Optional model overrides via env: ANTHROPIC_MODEL, ELEVENLABS_MUSIC_MODEL, VEO_MODEL, VEO_ENDPOINT, VEO_POLL_INTERVAL_MS, VEO_POLL_TIMEOUT_MS, FAL_OMNIHUMAN_MODEL, PROMO_VIDEO_LOG_LEVEL.
License
MIT — see LICENSE.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.