mcp-sora 2026.3.20.3

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 Your API Token

1. Sign up at AceDataCloud Platform
2. Go to the API documentation page
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://sora.mcp.acedata.cloud/mcp

All requests require a Bearer token. Use the API token from Step 1.

Claude Desktop

Add to your config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "sora": {
      "type": "streamable-http",
      "url": "https://sora.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Cursor / Windsurf

Add to your MCP config (.cursor/mcp.json or .windsurf/mcp.json):

{
  "mcpServers": {
    "sora": {
      "type": "streamable-http",
      "url": "https://sora.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

VS Code (Copilot)

Add to your VS Code MCP config (.vscode/mcp.json):

{
  "servers": {
    "sora": {
      "type": "streamable-http",
      "url": "https://sora.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Or install the Ace Data Cloud MCP extension for VS Code, which bundles all 11 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:

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

cURL Test

# Health check (no auth required)
curl https://sora.mcp.acedata.cloud/health

# MCP initialize
curl -X POST https://sora.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:

# Install from PyPI
pip install mcp-sora
# or
uvx mcp-sora

# Set your API token
export ACEDATACLOUD_API_TOKEN="your_token_here"

# Run (stdio mode for Claude Desktop / local clients)
mcp-sora

# Run (HTTP mode for remote access)
mcp-sora --transport http --port 8000

Claude Desktop (Local)

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

Docker (Self-Hosting)

docker pull ghcr.io/acedatacloud/mcp-sora:latest
docker run -p 8000:8000 ghcr.io/acedatacloud/mcp-sora: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
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
├── 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 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