mcp-luma-dream-machine

mcp-luma-dream-machine

Create videos and images using Luma AI, this MCP server handles all API functionality for Luma Dream Machine from Claude Desktop.

Category
Visit Server

README

Luma AI MCP Server 🎥

A Model Context Protocol server for Luma AI's Dream Machine API.

Overview

This MCP server integrates with Luma AI's Dream Machine API (v1) to provide tools for generating, managing, and manipulating AI-generated videos and images via Large Language Models. It implements the Model Context Protocol (MCP) to enable seamless interaction between AI assistants and Luma's creative tools.

Features ✨

  • Text-to-video generation
  • Advanced video generation with keyframes
  • Image-to-video conversion
  • Video extension and interpolation
  • Image generation with reference images
  • Audio addition to videos
  • Video upscaling
  • Credit management
  • Generation tracking and status checking

Tools 🛠️

  1. ping

    • Check if the Luma API is running
    • No parameters required
  2. create_generation

    • Creates a new video generation
    • Input:
      • prompt (string, required): Text description of the video to generate
      • model (string, optional): Model to use (default: "ray-2")
        • Available models: "ray-1-6", "ray-2", "ray-flash-2"
      • resolution (string, optional): Video resolution (choices: "540p", "720p", "1080p", "4k")
      • duration (string, optional): Video duration (only "5s" and "9s" are currently supported)
      • aspect_ratio (string, optional): Video aspect ratio (e.g., "16:9", "1:1", "9:16", "4:3", "3:4", "21:9", "9:21")
      • loop (boolean, optional): Whether to make the video loop
      • keyframes (object, optional): Start and end frames for advanced video generation:
        • frame0 and/or frame1 with either:
          • {"type": "image", "url": "image_url"} for image keyframes
          • {"type": "generation", "id": "generation_id"} for video keyframes
  3. get_generation

    • Gets the status of a generation
    • Input:
      • generation_id (string, required): ID of the generation to check
    • Output includes:
      • Generation ID
      • State (queued, dreaming, completed, failed)
      • Failure reason (if failed)
      • Video URL (if completed)
  4. list_generations

    • Lists all generations
    • Input:
      • limit (number, optional): Maximum number of generations to return (default: 10)
      • offset (number, optional): Number of generations to skip
  5. delete_generation

    • Deletes a generation
    • Input:
      • generation_id (string, required): ID of the generation to delete
  6. upscale_generation

    • Upscales a video generation to higher resolution
    • Input:
      • generation_id (string, required): ID of the generation to upscale
      • resolution (string, required): Target resolution for the upscaled video (one of "540p", "720p", "1080p", or "4k")
    • Note:
      • The generation must be in a completed state to be upscaled
      • The target resolution must be higher than the original generation's resolution
      • Each generation can only be upscaled once
  7. add_audio

    • Adds AI-generated audio to a video generation
    • Input:
      • generation_id (required): The ID of the generation to add audio to
      • prompt (required): The prompt for the audio generation
      • negative_prompt (optional): The negative prompt for the audio generation
      • callback_url (optional): URL to notify when the audio processing is complete
  8. generate_image

    • Generates an image from a text prompt with optional reference images
    • Input:
      • prompt (string, required): Text description of the image to generate
      • model (string, optional): Model to use for image generation (default: "photon-1")
        • Available models: "photon-1", "photon-flash-1"
      • aspect_ratio (string, optional): Image aspect ratio (same options as video)
      • image_ref (array, optional): Reference images to guide generation
        • Each ref: {"url": "image_url", "weight": optional_float}
      • style_ref (array, optional): Style reference images
        • Each ref: {"url": "image_url", "weight": optional_float}
      • character_ref (object, optional): Character reference images
        • Format: {"identity_name": {"images": ["url1", "url2", ...]}}
      • modify_image_ref (object, optional): Image to modify
        • Format: {"url": "image_url", "weight": optional_float}
  9. get_credits

    • Gets credit information for the current user
    • No parameters required
    • Returns available credit balance in USD cents
  10. get_camera_motions

    • Gets all supported camera motions
    • No parameters required
    • Returns: List of available camera motion strings

Setup for Claude Desktop 🖥️

  1. Get your Luma API key from Luma AI (sign up or log in to get your API key)

  2. Add this to your Claude Desktop configuration file:

    • On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • On Windows: %APPDATA%\Claude\claude_desktop_config.json
    {
      "mcpServers": {
        "luma": {
          "command": "uv",
          "args": [
            "run",
            "--project",
            "/path/to/your/luma-ai-mcp-server",
            "-m",
            "luma_ai_mcp_server"
          ],
          "env": {
            "LUMA_API_KEY": "your-luma-api-key-here"
          }
        }
      }
    }
    

    Replace:

    • /path/to/your/luma-ai-mcp-server with the actual path to your server directory
    • your-luma-api-key-here with your actual Luma API key
  3. Restart Claude Desktop

  4. That's it! You can now use Luma AI tools directly in Claude Desktop conversations.

Quick Troubleshooting 🛠️

If you're having issues:

  1. Check your API key is correct
  2. Make sure the path to the server is correct
  3. View logs with: tail -n 20 -f ~/Library/Logs/Claude/mcp*.log

Advanced Video Generation Types 🎬

The Luma API supports various types of advanced video generation through keyframes:

  1. Starting from an image: Provide frame0 with type: "image" and an image URL
  2. Ending with an image: Provide frame1 with type: "image" and an image URL
  3. Extending a video: Provide frame0 with type: "generation" and a generation ID
  4. Reverse extending a video: Provide frame1 with type: "generation" and a generation ID
  5. Interpolating between videos: Provide both frame0 and frame1 with type: "generation" and generation IDs

API Limitations and Notes 📝

  • Duration: Currently, the API only supports durations of "5s" or "9s"
  • Resolution: Valid values are "540p", "720p", "1080p", and "4k"
  • Models:
    • Video generation:
      • "ray-2" (default) - Best quality, slower
      • "ray-flash-2" - Faster generation
      • "ray-1-6" - Legacy model
    • Image generation:
      • "photon-1" (default) - Best quality, slower
      • "photon-flash-1" - Faster generation
  • Generation types: Video, image, and advanced (with keyframes)
  • Aspect Ratios: "1:1" (square), "16:9" (landscape), "9:16" (portrait), "4:3" (standard), "3:4" (standard portrait), "21:9" (ultrawide), "9:21" (ultrawide portrait)
  • States: "queued", "dreaming", "completed", "failed"
  • Upscaling:
    • Video generations can only be upscaled when they're in a "complete" state
    • Target resolution must be higher than the original generation's resolution
    • Each generation can only be upscaled once
  • API Key: Required in environment variables
  • API Version: Uses Dream Machine API v1

License 📄

MIT

Recommended Servers

playwright-mcp

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.

Official
Featured
TypeScript
Magic Component Platform (MCP)

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.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

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.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

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.

Official
Featured
TypeScript
Kagi MCP Server

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.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

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.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured