Videolab

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.binary to 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_promo writes per-slot intermediates (slot_*.mp4), a silent visuals.mp4, the audio mix, and the final output.mp4 — all under media/renders/<renderId>/.
• swap_music / swap_voiceover reuse the parent's visuals.mp4 and only re-mix audio. Typical wall time: ~2 seconds.
• swap_clip rebuilds the visuals stream + remixes audio. Typical wall time: ~5–10 seconds.
• Every render is a new renderId linked via parentId — 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.