Mcp Luma

LumaMCP

[PyPI version] [PyPI downloads] [Python 3.10+] [License: MIT] [MCP]

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

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:

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 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.
• 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.
• 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.
• 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.
• 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.

Cursor project config (OAuth):

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

VS Code-specific workspace config (OAuth):

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

Hosted API token

1. Open Ace Data Cloud → Applications. 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.
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]

[Create a separately named key with optional restrictions]

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.

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:

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:

{  "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:

{  "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:

{  "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 · JetBrains · Zed.

Local stdio

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

python -m pip install mcp-lumaexport 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 on PATH:

{  "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 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

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 repositorygit clone https://github.com/AceDataCloud/LumaMCP.gitcd LumaMCP
# Create virtual environmentpython -m venv .venvsource .venv/bin/activate  # or `.venv\Scripts\activate` on Windows
# Install with dev dependenciespip install -e ".[dev,test]"

Run Tests

# Run unit testspytest
# Run with coveragepytest --cov=core --cov=tools
# Run integration tests (requires API token)pytest tests/test_integration.py -m integration

Code Quality

# Format coderuff format .
# Lint coderuff check .
# Type checkmypy core tools

Build & Publish

# Install build dependenciespip install -e ".[release]"
# Build packagepython -m build
# Upload to PyPItwine 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

Documentation

Documentation

License

MIT License - see LICENSE for details.

Links

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

Made with love by AceDataCloud