io.github.AceDataCloudPublished by GitHub account AceDataCloudv2026.11.1.0Updated Nov 1, 2026
MCP server for Luma Dream Machine AI video generation
Luma Dream Machine video generation via AceDataCloud: lets an assistant create, animate and extend AI videos and track generation tasks.
Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.
Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.
Read https://sourceweft.com/mcp/io.github.AceDataCloud%2Fmcp-luma/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.Add this to your client's mcpServers config. The client completes OAuth sign-in on first connect.
{
"mcpServers": {
"luma": {
"type": "http",
"url": "https://luma.mcp.acedata.cloud/mcp"
}
}
}[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.
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.
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 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://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://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 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):
VS Code-specific workspace config (OAuth):
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:
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:
Cursor uses a different environment-variable syntax in ~/.cursor/mcp.json or an uncommitted project config:
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:
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.
Install the package and give the local process an API token:
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:
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.
https://luma.mcp.acedata.cloud/health returning {"status":"ok"} checks endpoint reachability only.luma_list_actions is a reference tool; it does not verify downstream API access or balance.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.
This server wraps the AceDataCloud Luma API:
Contributions are welcome! Please:
git checkout -b feature/amazing)git commit -m 'Add amazing feature')git push origin feature/amazing)MIT License - see LICENSE for details.
Made with love by AceDataCloud
Source: README.md at commit 0093ba0
List all available aspect ratios for Luma video generation. Shows all available aspect ratio options with their use cases. Use this to understand which aspect ratio to choose for your video. Returns: Table of all aspect ratios with their descriptions and use cases.
Takes no parameters.
List all available Luma API actions and corresponding tools. Reference guide for what each action does and which tool to use. Helpful for understanding the full capabilities of the Luma MCP. Returns: Categorized list of all actions and their corresponding tools.
Takes no parameters.
Query the status and result of a video generation task. Use this to check if a generation is complete and retrieve the resulting video URLs, thumbnails, and other metadata. Use this when: - You want to check if a generation has completed - You need to retrieve video URLs from a previous generation - You want to get the full details of a generated video Task states: - 'pending': Generation is still in progress - 'completed': Generation finished successfully - 'failed': Generation failed (check error message) Returns: Task status and generated video information including URLs, dimensions, and thumbnail.
task_idrequiredQuery multiple video generation tasks at once. Efficiently check the status of multiple tasks in a single request. More efficient than calling luma_get_task multiple times. Use this when: - You have multiple pending generations to check - You want to get status of several videos at once - You're tracking a batch of generations Returns: Status and video information for all queried tasks.
task_idsrequiredGenerate AI video from a text prompt using Luma Dream Machine. This is the simplest way to create video - just describe what you want and Luma will generate a high-quality AI video. Use this when: - You want to create a video from a text description - You don't have reference images - You want quick video generation For using reference images (start/end frames), use luma_generate_video_from_image instead. Returns: Task ID and generated video information including URLs, dimensions, and thumbnail.
looppromptrequiredtimeoutenhancementaspect_ratiocallback_urlGenerate AI video using reference images as start and/or end frames. This allows you to control the video by specifying what the first frame and/or last frame should look like. Luma will generate smooth motion between them. Use this when: - You have a specific image you want to animate - You want to create a video transition between two images - You need precise control over the video's visual content At least one of start_image_url or end_image_url must be provided. Returns: Task ID and generated video information including URLs, dimensions, and thumbnail.
looppromptrequiredtimeoutenhancementaspect_ratiocallback_urlend_image_urlstart_image_urlExtend an existing video with additional content. This allows you to continue a previously generated video, adding more motion and content after the original video ends. Use this when: - A generated video is too short and you want to add more - You want to continue the story or motion from a previous video - You're building a longer video piece by piece Returns: Task ID and the extended video information.
promptrequiredvideo_idrequiredcallback_urlend_image_urlExtend an existing video using its URL. Similar to luma_extend_video, but uses the video URL instead of video ID. This is useful when you have the video URL but not the original video ID. Use this when: - You have the video URL from a previous generation - You want to extend a video but don't have the video_id Returns: Task ID and the extended video information.
promptrequiredvideo_urlrequiredcallback_urlend_image_url100% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
{ "mcpServers": { "luma": {"url": "https://luma.mcp.acedata.cloud/mcp"} }}{ "servers": { "luma": {"type": "http", "url": "https://luma.mcp.acedata.cloud/mcp"} }}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"{ "mcpServers": { "luma": { "type": "http", "url": "https://luma.mcp.acedata.cloud/mcp", "headers": {"Authorization": "Bearer ${ACEDATACLOUD_API_TOKEN}"} } }}{ "mcpServers": { "luma": { "url": "https://luma.mcp.acedata.cloud/mcp", "headers": {"Authorization": "Bearer ${env:ACEDATACLOUD_API_TOKEN}"} } }}{ "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}"} } }}python -m pip install mcp-lumaexport ACEDATACLOUD_API_TOKEN='YOUR_API_TOKEN'mcp-luma{ "mcpServers": { "luma": { "command": "uvx", "args": ["mcp-luma"], "env": {"ACEDATACLOUD_API_TOKEN": "YOUR_API_TOKEN"} } }}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"]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]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]mcp-luma --help
Options: --version Show version --transport Transport mode: stdio (default) or http --port Port for HTTP transport (default: 8000)# 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 unit testspytest
# Run with coveragepytest --cov=core --cov=tools
# Run integration tests (requires API token)pytest tests/test_integration.py -m integration# Format coderuff format .
# Lint coderuff check .
# Type checkmypy core tools# Install build dependenciespip install -e ".[release]"
# Build packagepython -m build
# Upload to PyPItwine upload dist/*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