LumaMCP

# LumaMCP

<!-- mcp-name: io.github.AceDataCloud/mcp-luma -->

[![PyPI version](https://img.shields.io/pypi/v/mcp-luma.svg)](https://pypi.org/project/mcp-luma/)
[![PyPI downloads](https://img.shields.io/pypi/dm/mcp-luma.svg)](https://pypi.org/project/mcp-luma/)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![MCP](https://img.shields.io/badge/MCP-Compatible-green.svg)](https://modelcontextprotocol.io)

A [Model Context Protocol (MCP)](https://modelcontextprotocol.io) server for AI video generation using [Luma Dream Machine](https://lumalabs.ai/dream-machine) through the [AceDataCloud API](https://platform.acedata.cloud).

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

| Tool | Description |
|------|-------------|
| `luma_generate_video` | Generate AI video from a text prompt using Luma Dream Machine. |
| `luma_generate_video_from_image` | Generate AI video using reference images as start and/or end frames. |
| `luma_extend_video` | Extend an existing video with additional content. |
| `luma_extend_video_from_url` | Extend an existing video using its URL. |
| `luma_get_task` | Query the status and result of a video generation task. |
| `luma_get_tasks_batch` | Query multiple video generation tasks at once. |
| `luma_list_aspect_ratios` | List all available aspect ratios for Luma video generation. |
| `luma_list_actions` | List all available Luma API actions and corresponding tools. |

## Quick Start

### 1. Get Your API Token

1. Sign up at [AceDataCloud Platform](https://platform.acedata.cloud)
2. Go to the [API documentation page](https://platform.acedata.cloud/documents/5bd3597d-1ff8-44ad-a580-b66b48393e7f)
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](https://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):

```json
{
  "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`):

```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`):

```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](https://marketplace.visualstudio.com/items?itemName=acedatacloud.acedatacloud-mcp) for VS Code, which registers the hosted 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:

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


#### Claude Code

Claude Code supports MCP servers natively:

```bash
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`:

```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`):

```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:

```json
{
  "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:

```json
{
  "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`:

```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`):

```json
{
  "language_models": {
    "mcp_servers": {
      "luma": {
        "url": "https://luma.mcp.acedata.cloud/mcp",
        "headers": {
          "Authorization": "Bearer YOUR_API_TOKEN"
        }
      }
    }
  }
}
```

#### cURL Test

```bash
# 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:

```bash
# 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)

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

#### Docker (Self-Hosting)

```bash
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                     |
| ----------