mcp-sora 2026.3.1.0

MCP Server for Sora AI Video Generation via AceDataCloud API

MCP Sora

A Model Context Protocol (MCP) server for AI video generation using Sora through the AceDataCloud API.

Generate AI videos directly from Claude, VS Code, or any MCP-compatible client.

Features

• Text-to-Video - Generate videos from text descriptions
• Image-to-Video - Animate images and create videos from reference images
• Character Videos - Reuse characters across different scenes
• Async Generation - Webhook callbacks for production workflows
• Multiple Orientations - Landscape, portrait, and square videos
• Task Tracking - Monitor generation progress and retrieve results

Quick Start

1. Get API Token

Get your API token from AceDataCloud Platform:

1. Sign up or log in
2. Navigate to Sora Videos API
3. Click "Acquire" to get your token

2. Install

# Clone the repository
git clone https://github.com/AceDataCloud/mcp-sora.git
cd mcp-sora

# Install with pip
pip install -e .

# Or with uv (recommended)
uv pip install -e .

3. Configure

# Copy example environment file
cp .env.example .env

# Edit with your API token
echo "ACEDATACLOUD_API_TOKEN=your_token_here" > .env

4. Run

# Run the server
mcp-sora

# Or with Python directly
python main.py

Claude Desktop Integration

Add to your Claude Desktop configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "sora": {
      "command": "mcp-sora",
      "env": {
        "ACEDATACLOUD_API_TOKEN": "your_api_token_here"
      }
    }
  }
}

Or if using uv:

{
  "mcpServers": {
    "sora": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/mcp-sora", "mcp-sora"],
      "env": {
        "ACEDATACLOUD_API_TOKEN": "your_api_token_here"
      }
    }
  }
}

Available Tools

Video Generation

Tool	Description
sora_generate_video	Generate video from a text prompt
sora_generate_video_from_image	Generate video from reference images
sora_generate_video_with_character	Generate video with a character from reference video
sora_generate_video_async	Generate video with callback notification

Tasks

Tool	Description
sora_get_task	Query a single task status
sora_get_tasks_batch	Query multiple tasks at once

Information

Tool	Description
sora_list_models	List available Sora models
sora_list_actions	List available API actions

Usage Examples

Generate Video from Prompt

User: Create a video of a sunset over mountains

Claude: I'll generate a sunset video for you.
[Calls sora_generate_video with prompt="A beautiful sunset over mountains..."]

Generate from Image

User: Animate this image of a city skyline

Claude: I'll bring this image to life.
[Calls sora_generate_video_from_image with image_urls and prompt]

Character-based Video

User: Use the robot character in a new scene

Claude: I'll create a new scene with the robot character.
[Calls sora_generate_video_with_character with character_url and prompt]

Available Models

Model	Max Duration	Quality	Features
sora-2	15 seconds	Good	Standard generation
sora-2-pro	25 seconds	Best	Higher quality, longer videos

Video Options

Size:

• small - Lower resolution, faster generation
• large - Higher resolution (recommended)

Orientation:

• landscape - 16:9 (YouTube, presentations)
• portrait - 9:16 (TikTok, Instagram Stories)
• square - 1:1 (Instagram posts)

Duration:

• 10 seconds - All models
• 15 seconds - All models
• 25 seconds - sora-2-pro only

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
SORA_DEFAULT_MODEL	Default model	sora-2
SORA_DEFAULT_SIZE	Default video size	large
SORA_DEFAULT_DURATION	Default duration (seconds)	15
SORA_DEFAULT_ORIENTATION	Default orientation	landscape
SORA_REQUEST_TIMEOUT	Request timeout (seconds)	3600
LOG_LEVEL	Logging level	INFO

Command Line Options

mcp-sora --help

Options:
  --version          Show version
  --transport        Transport mode: stdio (default) or http
  --port             Port for HTTP transport (default: 8000)

Development

Setup Development Environment

# Clone repository
git clone https://github.com/AceDataCloud/mcp-sora.git
cd mcp-sora

# 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

# 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

# Format code
ruff format .

# Lint code
ruff check .

# Type check
mypy core tools

Build & Publish

# Install build dependencies
pip install -e ".[release]"

# Build package
python -m build

# Upload to PyPI
twine upload dist/*

Project Structure

MCPSora/
├── core/                   # Core modules
│   ├── __init__.py
│   ├── client.py          # HTTP client for Sora 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 prompt templates
│   └── __init__.py
├── tests/                  # Test suite
│   ├── conftest.py
│   ├── test_client.py
│   ├── test_config.py
│   ├── test_integration.py
│   └── test_utils.py
├── .env.example           # Environment template
├── .gitignore
├── CHANGELOG.md
├── LICENSE
├── main.py                # Entry point
├── pyproject.toml         # Project configuration
└── README.md

API Reference

This server wraps the AceDataCloud Sora API:

• Sora Videos API - Video generation
• Sora 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

License

MIT License - see LICENSE for details.

Links

• AceDataCloud Platform
• Sora Official
• Model Context Protocol
• MCP Python SDK

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Made with love by AceDataCloud