Luma AI MCP Server 🎥

Luma AI MCP Server 🎥

Mirror of

MCP-Mirror

Research & Data
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

Crypto Price & Market Analysis MCP Server

Crypto Price & Market Analysis MCP Server

A Model Context Protocol (MCP) server that provides comprehensive cryptocurrency analysis using the CoinCap API. This server offers real-time price data, market analysis, and historical trends through an easy-to-use interface.

Featured
TypeScript
MCP PubMed Search

MCP PubMed Search

Server to search PubMed (PubMed is a free, online database that allows users to search for biomedical and life sciences literature). I have created on a day MCP came out but was on vacation, I saw someone post similar server in your DB, but figured to post mine.

Featured
Python
dbt Semantic Layer MCP Server

dbt Semantic Layer MCP Server

A server that enables querying the dbt Semantic Layer through natural language conversations with Claude Desktop and other AI assistants, allowing users to discover metrics, create queries, analyze data, and visualize results.

Featured
TypeScript
mixpanel

mixpanel

Connect to your Mixpanel data. Query events, retention, and funnel data from Mixpanel analytics.

Featured
TypeScript
Sequential Thinking MCP Server

Sequential Thinking MCP Server

This server facilitates structured problem-solving by breaking down complex issues into sequential steps, supporting revisions, and enabling multiple solution paths through full MCP integration.

Featured
Python
Nefino MCP Server

Nefino MCP Server

Provides large language models with access to news and information about renewable energy projects in Germany, allowing filtering by location, topic (solar, wind, hydrogen), and date range.

Official
Python
Vectorize

Vectorize

Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.

Official
JavaScript
Mathematica Documentation MCP server

Mathematica Documentation MCP server

A server that provides access to Mathematica documentation through FastMCP, enabling users to retrieve function documentation and list package symbols from Wolfram Mathematica.

Local
Python
kb-mcp-server

kb-mcp-server

An MCP server aimed to be portable, local, easy and convenient to support semantic/graph based retrieval of txtai "all in one" embeddings database. Any txtai embeddings db in tar.gz form can be loaded

Local
Python
Research MCP Server

Research MCP Server

The server functions as an MCP server to interact with Notion for retrieving and creating survey data, integrating with the Claude Desktop Client for conducting and reviewing surveys.

Local
Python