# LumaMCP

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

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[![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?utm_source=github&utm_medium=referral&utm_campaign=evergreen&utm_content=luma_mcp_readme_platform).

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. |

## Connect: hosted OAuth, API token, or local stdio

The hosted endpoint is `https://luma.mcp.acedata.cloud/mcp`. Choose one route for the MCP client:

| Route | When to use it | Credential setup |
|---|---|---|
| Hosted OAuth | The client supports remote MCP OAuth | Add only the URL, then sign in to AceDataCloud and approve access. No token needs to be pasted into client configuration. |
| Hosted API token | The client cannot finish OAuth, or you need an explicit integration credential | Send an AceDataCloud API token in the `Authorization: Bearer …` header. Keep it in a local secret store or environment variable. |
| Local stdio | The client runs a local MCP process | Install `mcp-luma` and pass `ACEDATACLOUD_API_TOKEN` to that process. It still calls the AceDataCloud API. |

The hosted service advertises OAuth metadata and Dynamic Client Registration (DCR). **DCR registers the client application; it is not an API key.** OAuth signs you in and the client sends the resulting Bearer token; it may reuse or create an API credential for the account. Browser sign-in still requires an AceDataCloud account. The hosted service can be metered: review [current service documentation](https://platform.acedata.cloud/documents/luma-mcp?utm_source=github&utm_medium=referral&utm_campaign=evergreen&utm_content=luma_mcp_readme_quick_start) and displayed pricing before a real operation. Do not configure both an OAuth login and a fixed `Authorization` header for the same server.

### Hosted OAuth examples

- **Claude and Claude Desktop chat:** Add a remote custom connector in `Customize → Connectors → Add custom connector`, enter `https://luma.mcp.acedata.cloud/mcp`, select sign-in, and choose **Register automatically** if Claude asks how to register its OAuth client. Complete consent. Claude Desktop's local `claude_desktop_config.json` is a separate setup. [Claude connector guide](https://support.claude.com/en/articles/11175166-get-started-with-custom-connectors-using-remote-mcp).
- **Claude Code:** `claude mcp add --transport http --scope user luma https://luma.mcp.acedata.cloud/mcp`, then `claude mcp login luma`. Check `/mcp`. [Claude Code MCP guide](https://code.claude.com/docs/en/mcp).
- **Cursor:** Add a remote server with only `https://luma.mcp.acedata.cloud/mcp`. For a project, merge the entry below into `<project>/.cursor/mcp.json`; for personal use, use `~/.cursor/mcp.json`. [Cursor MCP guide](https://cursor.com/docs/mcp).
- **VS Code / Copilot:** Run **MCP: Add Server**, select HTTP, enter `https://luma.mcp.acedata.cloud/mcp`, then finish the browser sign-in. New portable workspace configs use `<project>/.mcp.json`; the VS Code-specific format below uses `<project>/.vscode/mcp.json` or the user profile. Check **MCP: List Servers**. [VS Code MCP setup](https://code.visualstudio.com/docs/agent-customization/mcp-servers).
- **Codex:** `codex mcp add luma --url https://luma.mcp.acedata.cloud/mcp`, then `codex mcp login luma`. Its user settings are in `~/.codex/config.toml`. [Official Codex MCP guide](https://developers.openai.com/codex/mcp/).

Cursor project config (OAuth):

```json
{
  "mcpServers": {
    "luma": {"url": "https://luma.mcp.acedata.cloud/mcp"}
  }
}
```

VS Code-specific workspace config (OAuth):

```json
{
  "servers": {
    "luma": {"type": "http", "url": "https://luma.mcp.acedata.cloud/mcp"}
  }
}
```

### Hosted API token

<!-- BEGIN TOKEN ONBOARDING -->
1. Open [Ace Data Cloud → Applications](https://platform.acedata.cloud/console/applications?utm_source=github&utm_medium=referral&utm_campaign=evergreen&utm_content=luma_mcp_readme_api_key). Sign in, or register an account first.
2. Find **General application**. If it has not been created, choose **Initialize General Application**. A general key can use the services available to your account; a dedicated application's key is limited to its service. Check the required service/model access and [current pricing](https://platform.acedata.cloud/models?utm_source=github&utm_medium=referral&utm_campaign=evergreen&utm_content=luma_mcp_readme_models).
3. Copy the **API Key** from the application card, or open **Keys / Manage Keys → Create** to create a separate key for this integration. Give it a recognizable name so you can revoke or rotate it independently.
4. When creating a key, review **Expiration Time**, **Set Usage Limitation**, and **Allowed APIs**. Enable only the restrictions you need, allow the operations you will call, then click **Create** and copy the token from the key list. Task lookup does not grant permission to submit a generation. Confirm the application's balance can cover your first request; any trial allowance is whatever your account currently displays.
5. Copy only the token value, without quotation marks, spaces, or `Bearer `. An API Key / API Token field expects this raw value; only a manually configured HTTP `Authorization` header needs the `Bearer ` prefix. A `platform-...` account-management token cannot replace a generation API key.

![Copy an application API key or open Manage Keys](https://raw.githubusercontent.com/AceDataCloud/NanoBananaDify/2522b2f57210597af2db866445319334a1677a23/_assets/tutorial/get-api-key-en.png)

![Create a separately named key with optional restrictions](https://raw.githubusercontent.com/AceDataCloud/NanoBananaDify/2522b2f57210597af2db866445319334a1677a23/_assets/tutorial/create-api-key-en.png)

Save the key in the client's credential store or the environment variable shown below. Never put it in a prompt, shared workflow, screenshot, or Git commit. If authentication fails, check the full copied value, application/service access, expiration, usage limit, and balance before creating another key.
<!-- END TOKEN ONBOARDING -->

A fixed Bearer header bypasses OAuth. If the header is invalid, remove it before trying browser sign-in; keep the saved credential private.

For Claude Code, the shell expands the token when you add the server; treat the saved user MCP config as a secret:

```bash
export ACEDATACLOUD_API_TOKEN='YOUR_API_TOKEN'
claude mcp add --transport http --scope user luma https://luma.mcp.acedata.cloud/mcp \
  --header "Authorization: Bearer $ACEDATACLOUD_API_TOKEN"
```

For a Claude Code project config, put a variable reference in `<project>/.mcp.json` and set that variable in the environment that launches Claude Code:

```json
{
  "mcpServers": {
    "luma": {
      "type": "http",
      "url": "https://luma.mcp.acedata.cloud/mcp",
      "headers": {"Authorization": "Bearer ${ACEDATACLOUD_API_TOKEN}"}
    }
  }
}
```

Cursor uses a different environment-variable syntax in `~/.cursor/mcp.json` or an uncommitted project config:

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

In VS Code, run **MCP: Open User Configuration** and merge this server plus its masked input; `${input:...}` is for VS Code's user/workspace format and is not portable to the Agent Host `.mcp.json` format:

```json
{
  "inputs": [
    {"id": "acedata-luma-token", "type": "promptString", "description": "AceDataCloud API token", "password": true}
  ],
  "servers": {
    "luma": {
      "type": "http",
      "url": "https://luma.mcp.acedata.cloud/mcp",
      "headers": {"Authorization": "Bearer ${input:acedata-luma-token}"}
    }
  }
}
```

For **Cline**, use its MCP configuration UI or CLI file `~/.cline/data/settings/cline_mcp_settings.json`; its remote transport value is `streamableHttp`. For **JetBrains AI Assistant**, add a remote URL from **Settings → Tools → AI Assistant → Model Context Protocol (MCP)**. For **Zed**, use a `context_servers` entry with the URL only for OAuth or add a local Bearer header. These clients have different configuration schemas; follow their current UI rather than copying another client's JSON. [Cline](https://docs.cline.bot/mcp/mcp-overview) · [JetBrains](https://www.jetbrains.com/help/ai-assistant/mcp.html) · [Zed](https://zed.dev/docs/ai/mcp).

### Local stdio

Install the package and give the local process an API token:

```bash
python -m pip install mcp-luma
export ACEDATACLOUD_API_TOKEN='YOUR_API_TOKEN'
mcp-luma
```

For Claude Desktop local MCP, merge this entry into the file opened by its developer settings (`~/Library/Application Support/Claude/claude_desktop_config.json` on macOS). `uvx` requires [uv](https://docs.astral.sh/uv/) on `PATH`:

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

Keep this user-level file private. Self-hosted HTTP uses `mcp-luma --transport http --port 8000`; expose it only with suitable network and TLS controls. Local execution still calls the AceDataCloud API.

### Check before using the service

1. `https://luma.mcp.acedata.cloud/health` returning `{"status":"ok"}` checks endpoint reachability only.
2. Confirm that the MCP client loads tools. `luma_list_actions` is a reference tool; it does not verify downstream API access or balance.
3. If you need a full API check, call `luma_generate_video` with your own valid input after reviewing [current service documentation](https://platform.acedata.cloud/documents/luma-mcp?utm_source=github&utm_medium=referral&utm_campaign=evergreen&utm_content=luma_mcp_readme_quick_start) and displayed pricing. If the result contains a task ID, call `luma_get_task` on that same ID until terminal success or failure. Do not resubmit the operation just to check progress.

For **401**, check which auth route the client used and whether the token or OAuth session is valid. A **403** may mean an account permission or content moderation failure; read the returned error. Insufficient balance and downstream service failures need their own diagnosis. A listed tool or submitted task does not prove a successful result.

## 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

```bash
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

```bash
# 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

```bash
# 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

```bash
# Format code
ruff format .

# Lint code
ruff check .

# Type check
mypy core tools
```

### Build & Publish

```bash
# 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](https://platform.acedata.cloud/documents/luma-videos?utm_source=github&utm_medium=referral&utm_campaign=evergreen&utm_content=luma_mcp_readme_documents_luma-videos):

- [Luma Videos API](https://platform.acedata.cloud/documents/luma-videos?utm_source=github&utm_medium=referral&utm_campaign=evergreen&utm_content=luma_mcp_readme_documents_luma-videos) - Video generation
- [Luma Tasks API](https://platform.acedata.cloud/documents/luma-tasks?utm_source=github&utm_medium=referral&utm_campaign=evergreen&utm_content=luma_mcp_readme_documents_luma-tasks) - 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

## Documentation

<!-- canonical-documentation -->
[Documentation](https://platform.acedata.cloud/documents/luma-mcp?utm_source=github&utm_medium=referral&utm_campaign=evergreen&utm_content=luma_mcp_readme_quick_start)

## License

MIT License - see [LICENSE](LICENSE) for details.

## Links

- [AceDataCloud Platform](https://platform.acedata.cloud?utm_source=github&utm_medium=referral&utm_campaign=evergreen&utm_content=luma_mcp_readme_platform)
- [Luma Dream Machine](https://lumalabs.ai/dream-machine)
- [Model Context Protocol](https://modelcontextprotocol.io)
- [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)

---

Made with love by [AceDataCloud](https://platform.acedata.cloud?utm_source=github&utm_medium=referral&utm_campaign=evergreen&utm_content=luma_mcp_readme_platform)