MCP Luma

A Model Context Protocol (MCP) server for AI video generation using Luma Dream Machine through the AceDataCloud API.

Generate AI videos directly from Claude, VS Code, or any MCP-compatible client.

Features

Text to Video - Create AI-generated videos from text prompts
Image to Video - Animate images with start/end frame control
Video Extension - Extend existing videos with additional content
Multiple Aspect Ratios - Support for 16:9, 9:16, 1:1, and more
Loop Videos - Create seamlessly looping animations
Clarity Enhancement - Optional video quality enhancement
Task Tracking - Monitor generation progress and retrieve results

Quick Start

1. Get Your API Token

Sign up at AceDataCloud Platform
Go to the API documentation page
Click "Acquire" to get your API token
Copy the token for use below

2. Use the Hosted Server (Recommended)

AceDataCloud hosts a managed MCP server — no local installation required.

Endpoint: https://luma.mcp.acedata.cloud/mc

All requests require a Bearer token. Use the API token from Step 1.

Claude.ai

Connect directly on Claude.ai with OAuth — no API token needed:

Go to Claude.ai Settings → Integrations → Add More
Enter the server URL: https://luma.mcp.acedata.cloud/mc
Complete the OAuth login flow
Start using the tools in your conversation

Claude Desktop

Add to your config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "luma": {
      "type": "streamable-http",
      "url": "https://luma.mcp.acedata.cloud/mc",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Cursor / Windsurf

Add to your MCP config (.cursor/mcp.json or .windsurf/mcp.json):

{
  "mcpServers": {
    "luma": {
      "type": "streamable-http",
      "url": "https://luma.mcp.acedata.cloud/mc",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

VS Code (Copilot)

Add to your VS Code MCP config (.vscode/mcp.json):

{
  "servers": {
    "luma": {
      "type": "streamable-http",
      "url": "https://luma.mcp.acedata.cloud/mc",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Or install the Ace Data Cloud MCP extension for VS Code, which bundles all 11 MCP servers with one-click setup.

JetBrains IDEs

Go to Settings → Tools → AI Assistant → Model Context Protocol (MCP)
Click Add → HTTP
Paste:

{
  "mcpServers": {
    "luma": {
      "url": "https://luma.mcp.acedata.cloud/mc",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

cURL Test

# Health check (no auth required)
curl https://luma.mcp.acedata.cloud/health

# MCP initialize
curl -X POST https://luma.mcp.acedata.cloud/mc \
  -H "Content-Type: application/json" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}'

3. Or Run Locally (Alternative)

If you prefer to run the server on your own machine:

# Install from PyPI
pip install mcp-luma
# or
uvx mcp-luma

# Set your API token
export ACEDATACLOUD_API_TOKEN="your_token_here"

# Run (stdio mode for Claude Desktop / local clients)
mcp-luma

# Run (HTTP mode for remote access)
mcp-luma --transport http --port 8000

Claude Desktop (Local)

{
  "mcpServers": {
    "luma": {
      "command": "uvx",
      "args": ["mcp-luma"],
      "env": {
        "ACEDATACLOUD_API_TOKEN": "your_token_here"
      }
    }
  }
}

Docker (Self-Hosting)

docker pull ghcr.io/acedatacloud/mcp-luma:latest
docker run -p 8000:8000 ghcr.io/acedatacloud/mcp-luma:latest

Clients connect with their own Bearer token — the server extracts the token from each request's Authorization header.

Available Tools

Video Generation

Tool	Description
`luma_generate_video`	Generate video from a text prompt
`luma_generate_video_from_image`	Generate video using reference images
`luma_extend_video`	Extend an existing video by ID
`luma_extend_video_from_url`	Extend an existing video by URL

Tasks

Tool	Description
`luma_get_task`	Query a single task status
`luma_get_tasks_batch`	Query multiple tasks at once

Information

Tool	Description
`luma_list_aspect_ratios`	List available aspect ratios
`luma_list_actions`	List available API actions

Usage Examples

Generate Video from Prompt

User: Create a video of waves on a beach

Claude: I'll generate a beach wave video for you.
[Calls luma_generate_video with prompt="Ocean waves gently crashing on sandy beach, sunset"]

Animate an Image

User: Animate this image: https://example.com/image.jpg

Claude: I'll create a video from your image.
[Calls luma_generate_video_from_image with start_image_url and appropriate prompt]

Extend a Video

User: Continue this video with more action

Claude: I'll extend the video with additional content.
[Calls luma_extend_video with video_id and new prompt]

Available Aspect Ratios

Aspect Ratio	Description	Use Case
`16:9`	Landscape (default)	YouTube, TV, presentations
`9:16`	Portrait	TikTok, Instagram Reels
`1:1`	Square	Instagram posts
`4:3`	Traditional	Classic video format
`3:4`	Portrait traditional	Portrait content
`21:9`	Ultrawide	Cinematic content
`9:21`	Tall ultrawide	Special vertical displays

Configuration

Environment Variables

Variable	Description	Default
`ACEDATACLOUD_API_TOKEN`	API token from AceDataCloud	Required
`ACEDATACLOUD_API_BASE_URL`	API base URL	`https://api.acedata.cloud`
`ACEDATACLOUD_OAUTH_CLIENT_ID`	OAuth client ID (hosted mode)	—
`ACEDATACLOUD_PLATFORM_BASE_URL`	Platform base URL	`https://platform.acedata.cloud`
`LUMA_DEFAULT_ASPECT_RATIO`	Default aspect ratio	`16:9`
`LUMA_REQUEST_TIMEOUT`	Request timeout in seconds	`1800`
`LOG_LEVEL`	Logging level	`INFO`

Command Line Options

mcp-luma --help

Options:
  --version          Show version
  --transport        Transport mode: stdio (default) or http
  --port             Port for HTTP transport (default: 8000)

Development

Setup Development Environment

# Clone repository
git clone https://github.com/AceDataCloud/mcp-luma.git
cd mcp-luma

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # or `.venv\Scripts\activate` on Windows

# Install with dev dependencies
pip install -e ".[dev,test]"

Run Tests

# Run unit tests
pytest

# Run with coverage
pytest --cov=core --cov=tools

# Run integration tests (requires API token)
pytest tests/test_integration.py -m integration

Code Quality

# Format code
ruff format .

# Lint code
ruff check .

# Type check
mypy core tools

Build & Publish

# Install build dependencies
pip install -e ".[release]"

# Build package
python -m build

# Upload to PyPI
twine upload dist/*

Project Structure

MCPLuma/
├── core/                   # Core modules
│   ├── __init__.py
│   ├── client.py          # HTTP client for Luma API
│   ├── config.py          # Configuration management
│   ├── exceptions.py      # Custom exceptions
│   ├── server.py          # MCP server initialization
│   ├── types.py           # Type definitions
│   └── utils.py           # Utility functions
├── tools/                  # MCP tool definitions
│   ├── __init__.py
│   ├── video_tools.py     # Video generation tools
│   ├── task_tools.py      # Task query tools
│   └── info_tools.py      # Information tools
├── prompts/                # MCP prompts
│   └── __init__.py        # Prompt templates
├── tests/                  # Test suite
│   ├── conftest.py
│   ├── test_client.py
│   ├── test_config.py
│   ├── test_integration.py
│   └── test_utils.py
├── deploy/                 # Deployment configs
│   └── production/
│       ├── deployment.yaml
│       ├── ingress.yaml
│       └── service.yaml
├── .env.example           # Environment template
├── .gitignore
├── CHANGELOG.md
├── Dockerfile             # Docker image for HTTP mode
├── docker-compose.yaml    # Docker Compose config
├── LICENSE
├── main.py                # Entry point
├── pyproject.toml         # Project configuration
└── README.md

API Reference

This server wraps the AceDataCloud Luma API:

Luma Videos API - Video generation
Luma Tasks API - Task queries

Contributing

Contributions are welcome! Please:

Fork the repository
Create a feature branch (git checkout -b feature/amazing)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing)
Open a Pull Request

License

MIT License - see LICENSE for details.

Links

Made with love by AceDataCloud

Name		Name	Last commit message	Last commit date
Latest commit History 63 Commits
.githooks		.githooks
.github		.github
core		core
deploy		deploy
jetbrains		jetbrains
prompts		prompts
tests		tests
tools		tools
vscode		vscode
.env.example		.env.example
.gitignore		.gitignore
.python-version		.python-version
.ruff.toml		.ruff.toml
CHANGELOG.md		CHANGELOG.md
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
docker-compose.yaml		docker-compose.yaml
glama.json		glama.json
main.py		main.py
pyproject.toml		pyproject.toml
server.json		server.json
smithery.yaml		smithery.yaml

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MCP Luma

Features

Quick Start

1. Get Your API Token

2. Use the Hosted Server (Recommended)

Claude.ai

Claude Desktop

Cursor / Windsurf

VS Code (Copilot)

JetBrains IDEs

cURL Test

3. Or Run Locally (Alternative)

Claude Desktop (Local)

Docker (Self-Hosting)

Available Tools

Video Generation

Tasks

Information

Usage Examples

Generate Video from Prompt

Animate an Image

Extend a Video

Available Aspect Ratios

Configuration

Environment Variables

Command Line Options

Development

Setup Development Environment

Run Tests

Code Quality

Build & Publish

Project Structure

API Reference

Contributing

License

Links

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