Luma MCP Server

LumaMCP

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

Tool Reference

Quick Start

1. Get Your API Token

1. Sign up at AceDataCloud Platform
2. Go to the API documentation page
3. Click "Acquire" to get your API token
4. 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/mcp

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:

1. Go to Claude.ai Settings → Integrations → Add More
2. Enter the server URL: https://luma.mcp.acedata.cloud/mcp
3. Complete the OAuth login flow
4. 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/mcp",
      "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/mcp",
      "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/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Or install the Ace Data Cloud MCP extension for VS Code, which bundles all 15 MCP servers with one-click setup.

JetBrains IDEs

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

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

Claude Code

Claude Code supports MCP servers natively:

claude mcp add luma --transport http https://luma.mcp.acedata.cloud/mcp \
  -h "Authorization: Bearer YOUR_API_TOKEN"

Or add to your project's .mcp.json:

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

Cline

Add to Cline's MCP settings (.cline/mcp_settings.json):

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

Amazon Q Developer

Add to your MCP configuration:

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

Roo Code

Add to Roo Code MCP settings:

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

Continue.dev

Add to .continue/config.yaml:

mcpServers:
  - name: luma
    type: streamable-http
    url: https://luma.mcp.acedata.cloud/mcp
    headers:
      Authorization: "Bearer YOUR_API_TOKEN"

Zed

Add to Zed's settings (~/.config/zed/settings.json):

{
  "language_models": {
    "mcp_servers": {
      "luma": {
        "url": "https://luma.mcp.acedata.cloud/mcp",
        "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/mcp \
  -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

Tasks

Information

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

Configuration

Environment Variables

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/LumaMCP.git
cd LumaMCP

# 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

LumaMCP/
├── 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:

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

License

MIT License - see LICENSE for details.

Links

• AceDataCloud Platform
• Luma Dream Machine
• Model Context Protocol
• MCP Python SDK

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Made with love by AceDataCloud