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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.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.

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.

A
license - permissive license
-
quality - not tested
C
maintenance

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