{
  "markdown": "# SoraMCP\n\n<!-- mcp-name: io.github.AceDataCloud/mcp-sora -->\n\n[![PyPI version](https://img.shields.io/pypi/v/mcp-sora.svg)](https://pypi.org/project/mcp-sora/)\n[![PyPI downloads](https://img.shields.io/pypi/dm/mcp-sora.svg)](https://pypi.org/project/mcp-sora/)\n[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![MCP](https://img.shields.io/badge/MCP-Compatible-green.svg)](https://modelcontextprotocol.io)\n\n> **Status:** This MCP integration is retired and is no longer actively offered.\n\nA [Model Context Protocol (MCP)](https://modelcontextprotocol.io) server for AI video generation using [Sora](https://openai.com/sora) through the [AceDataCloud API](https://platform.acedata.cloud).\n\nGenerate AI videos directly from Claude, VS Code, or any MCP-compatible client.\n\n## Features\n\n- **Text-to-Video** - Generate videos from text descriptions\n- **Image-to-Video** - Animate images and create videos from reference images\n- **Character Videos** - Reuse characters across different scenes\n- **Async Generation** - Webhook callbacks for production workflows\n- **Multiple Orientations** - Landscape and portrait videos\n- **Task Tracking** - Monitor generation progress and retrieve results\n\n## Tool Reference\n\n| Tool | Description |\n|------|-------------|\n| `sora_generate_video` | Generate an AI video from a text prompt using Sora. |\n| `sora_generate_video_from_image` | Generate an AI video from reference images using Sora (Image-to-Video). |\n| `sora_generate_video_with_character` | Generate an AI video featuring a character from a reference video. |\n| `sora_generate_video_async` | Generate an AI video asynchronously with callback notification. |\n| `sora_generate_video_v2` | Generate an AI video using Sora Version 2 (partner channel). |\n| `sora_generate_video_v2_async` | Generate an AI video asynchronously using Sora Version 2 with callback. |\n| `sora_get_task` | Query the status and result of a video generation task. |\n| `sora_get_tasks_batch` | Query multiple video generation tasks at once. |\n| `sora_list_models` | List all available Sora models and their capabilities. |\n| `sora_list_actions` | List all available Sora API actions and corresponding tools. |\n\n## Quick Start\n\n### 1. Get Your API Token\n\n1. Sign in to [AceDataCloud Platform](https://platform.acedata.cloud)\n2. Open Applications and copy an existing API token\n\n### 2. Use the Hosted Server (Recommended)\n\nAceDataCloud hosts a managed MCP server — **no local installation required**.\n\n**Endpoint:** `https://sora.mcp.acedata.cloud/mcp`\n\nAll requests require a Bearer token. Use the API token from Step 1.\n\n#### Claude.ai\n\nConnect directly on [Claude.ai](https://claude.ai) with OAuth — **no API token needed**:\n\n1. Go to Claude.ai **Settings → Integrations → Add More**\n2. Enter the server URL: `https://sora.mcp.acedata.cloud/mcp`\n3. Complete the OAuth login flow\n4. Start using the tools in your conversation\n\n#### Claude Desktop\n\nAdd to your config (`~/Library/Application Support/Claude/claude_desktop_config.json` on macOS):\n\n```json\n{\n  \"mcpServers\": {\n    \"sora\": {\n      \"type\": \"streamable-http\",\n      \"url\": \"https://sora.mcp.acedata.cloud/mcp\",\n      \"headers\": {\n        \"Authorization\": \"Bearer YOUR_API_TOKEN\"\n      }\n    }\n  }\n}\n```\n\n#### Cursor / Windsurf\n\nAdd to your MCP config (`.cursor/mcp.json` or `.windsurf/mcp.json`):\n\n```json\n{\n  \"mcpServers\": {\n    \"sora\": {\n      \"type\": \"streamable-http\",\n      \"url\": \"https://sora.mcp.acedata.cloud/mcp\",\n      \"headers\": {\n        \"Authorization\": \"Bearer YOUR_API_TOKEN\"\n      }\n    }\n  }\n}\n```\n\n#### VS Code (Copilot)\n\nAdd to your VS Code MCP config (`.vscode/mcp.json`):\n\n```json\n{\n  \"servers\": {\n    \"sora\": {\n      \"type\": \"streamable-http\",\n      \"url\": \"https://sora.mcp.acedata.cloud/mcp\",\n      \"headers\": {\n        \"Authorization\": \"Bearer YOUR_API_TOKEN\"\n      }\n    }\n  }\n}\n```\n\nOr install the [Ace Data Cloud MCP extension](https://marketplace.visualstudio.com/items?itemName=acedatacloud.acedatacloud-mcp) for VS Code, which registers the hosted MCP servers with one-click setup.\n\n#### JetBrains IDEs\n\n1. Go to **Settings → Tools → AI Assistant → Model Context Protocol (MCP)**\n2. Click **Add** → **HTTP**\n3. Paste:\n\n```json\n{\n  \"mcpServers\": {\n    \"sora\": {\n      \"url\": \"https://sora.mcp.acedata.cloud/mcp\",\n      \"headers\": {\n        \"Authorization\": \"Bearer YOUR_API_TOKEN\"\n      }\n    }\n  }\n}\n```\n\n\n#### Claude Code\n\nClaude Code supports MCP servers natively:\n\n```bash\nclaude mcp add sora --transport http https://sora.mcp.acedata.cloud/mcp \\\n  -h \"Authorization: Bearer YOUR_API_TOKEN\"\n```\n\nOr add to your project's `.mcp.json`:\n\n```json\n{\n  \"mcpServers\": {\n    \"sora\": {\n      \"type\": \"streamable-http\",\n      \"url\": \"https://sora.mcp.acedata.cloud/mcp\",\n      \"headers\": {\n        \"Authorization\": \"Bearer YOUR_API_TOKEN\"\n      }\n    }\n  }\n}\n```\n\n#### Cline\n\nAdd to Cline's MCP settings (`.cline/mcp_settings.json`):\n\n```json\n{\n  \"mcpServers\": {\n    \"sora\": {\n      \"type\": \"streamable-http\",\n      \"url\": \"https://sora.mcp.acedata.cloud/mcp\",\n      \"headers\": {\n        \"Authorization\": \"Bearer YOUR_API_TOKEN\"\n      }\n    }\n  }\n}\n```\n\n#### Amazon Q Developer\n\nAdd to your MCP configuration:\n\n```json\n{\n  \"mcpServers\": {\n    \"sora\": {\n      \"type\": \"streamable-http\",\n      \"url\": \"https://sora.mcp.acedata.cloud/mcp\",\n      \"headers\": {\n        \"Authorization\": \"Bearer YOUR_API_TOKEN\"\n      }\n    }\n  }\n}\n```\n\n#### Roo Code\n\nAdd to Roo Code MCP settings:\n\n```json\n{\n  \"mcpServers\": {\n    \"sora\": {\n      \"type\": \"streamable-http\",\n      \"url\": \"https://sora.mcp.acedata.cloud/mcp\",\n      \"headers\": {\n        \"Authorization\": \"Bearer YOUR_API_TOKEN\"\n      }\n    }\n  }\n}\n```\n\n#### Continue.dev\n\nAdd to `.continue/config.yaml`:\n\n```yaml\nmcpServers:\n  - name: sora\n    type: streamable-http\n    url: https://sora.mcp.acedata.cloud/mcp\n    headers:\n      Authorization: \"Bearer YOUR_API_TOKEN\"\n```\n\n#### Zed\n\nAdd to Zed's settings (`~/.config/zed/settings.json`):\n\n```json\n{\n  \"language_models\": {\n    \"mcp_servers\": {\n      \"sora\": {\n        \"url\": \"https://sora.mcp.acedata.cloud/mcp\",\n        \"headers\": {\n          \"Authorization\": \"Bearer YOUR_API_TOKEN\"\n        }\n      }\n    }\n  }\n}\n```\n\n#### cURL Test\n\n```bash\n# Health check (no auth required)\ncurl https://sora.mcp.acedata.cloud/health\n\n# MCP initialize\ncurl -X POST https://sora.mcp.acedata.cloud/mcp \\\n  -H \"Content-Type: application/json\" \\\n  -H \"Accept: application/json\" \\\n  -H \"Authorization: Bearer YOUR_API_TOKEN\" \\\n  -d '{\"jsonrpc\":\"2.0\",\"id\":1,\"method\":\"initialize\",\"params\":{\"protocolVersion\":\"2025-03-26\",\"capabilities\":{},\"clientInfo\":{\"name\":\"test\",\"version\":\"1.0\"}}}'\n```\n\n### 3. Or Run Locally (Alternative)\n\nIf you prefer to run the server on your own machine:\n\n```bash\n# Install from PyPI\npip install mcp-sora\n# or\nuvx mcp-sora\n\n# Set your API token\nexport ACEDATACLOUD_API_TOKEN=\"your_token_here\"\n\n# Run (stdio mode for Claude Desktop / local clients)\nmcp-sora\n\n# Run (HTTP mode for remote access)\nmcp-sora --transport http --port 8000\n```\n\n#### Claude Desktop (Local)\n\n```json\n{\n  \"mcpServers\": {\n    \"sora\": {\n      \"command\": \"uvx\",\n      \"args\": [\"mcp-sora\"],\n      \"env\": {\n        \"ACEDATACLOUD_API_TOKEN\": \"your_token_here\"\n      }\n    }\n  }\n}\n```\n\n#### Docker (Self-Hosting)\n\n```bash\ndocker pull ghcr.io/acedatacloud/mcp-sora:latest\ndocker run -p 8000:8000 ghcr.io/acedatacloud/mcp-sora:latest\n```\n\nClients connect with their own Bearer token — the server extracts the token from each request's `Authorization` header.\n\n## Available Tools\n\n### Video Generation\n\n| Tool                                 | Description                                          |\n| ------------------------------------ | ---------------------------------------------------- |\n| `sora_generate_video`                | Generate video from a text prompt                    |\n| `sora_generate_video_from_image`     | Generate video from reference images                 |\n| `sora_generate_video_with_character` | Generate video with a character from reference video |\n| `sora_generate_video_async`          | Generate video with callback notification            |\n\n### Tasks\n\n| Tool                   | Description                  |\n| ---------------------- | ---------------------------- |\n| `sora_get_task`        | Query a single task status   |\n| `sora_get_tasks_batch` | Query multiple tasks at once |\n\n### Information\n\n| Tool                | Description                |\n| ------------------- | -------------------------- |\n| `sora_list_models`  | List available Sora models |\n| `sora_list_actions` | List available API actions |\n\n## Usage Examples\n\n### Generate Video from Prompt\n\n```\nUser: Create a video of a sunset over mountains\n\nClaude: I'll generate a sunset video for you.\n[Calls sora_generate_video with prompt=\"A beautiful sunset over mountains...\"]\n```\n\n### Generate from Image\n\n```\nUser: Animate this image of a city skyline\n\nClaude: I'll bring this image to life.\n[Calls sora_generate_video_from_image with image_urls and prompt]\n```\n\n### Character-based Video\n\n```\nUser: Use the robot character in a new scene\n\nClaude: I'll create a new scene with the robot character.\n[Calls sora_generate_video_with_character with character_url and prompt]\n```\n\n## Available Models\n\n| Model        | Max Duration | Quality | Features                      |\n| ------------ | ------------ | ------- | ----------------------------- |\n| `sora-2`     | 15 seconds   | Good    | Standard generation           |\n| `sora-2-pro` | 25 seconds   | Best    | Higher quality, longer videos |\n\n### Video Options\n\n**Size:**\n\n- `small` - Lower resolution, faster generation\n- `large` - Higher resolution (recommended)\n\n**Orientation:**\n\n- `landscape` - 16:9 (YouTube, presentations)\n- `portrait` - 9:16 (TikTok, Instagram Stories)\n\n**Duration:**\n\n- `10` seconds - All models\n- `15` seconds - All models\n- `25` seconds - sora-2-pro only\n\n## Configuration\n\n### Environment Variables\n\n| Variable                    | Description                 | Default                     |\n| --------------------------- | --------------------------- | --------------------------- |\n| `ACEDATACLOUD_API_TOKEN`    | API token from AceDataCloud | **Required**                |\n| `ACEDATACLOUD_API_BASE_URL` | API base URL                | `https://api.acedata.cloud` |\n| `ACEDATACLOUD_OAUTH_CLIENT_ID`  | OAuth client ID (hosted mode) | —                           |\n| `ACEDATACLOUD_PLATFORM_BASE_URL` | Platform base URL            | `https://platform.acedata.cloud` |\n| `SORA_DEFAULT_MODEL`        | Default model               | `sora-2`                    |\n| `SORA_DEFAULT_SIZE`         | Default video size          | `large`                     |\n| `SORA_DEFAULT_DURATION`     | Default duration (seconds)  | `15`                        |\n| `SORA_DEFAULT_ORIENTATION`  | Default orientation         | `landscape`                 |\n| `SORA_REQUEST_TIMEOUT`      | Request timeout (seconds)   | `3600`                      |\n| `LOG_LEVEL`                 | Logging level               | `INFO`                      |\n\n### Command Line Options\n\n```bash\nmcp-sora --help\n\nOptions:\n  --version          Show version\n  --transport        Transport mode: stdio (default) or http\n  --port             Port for HTTP transport (default: 8000)\n```\n\n## Development\n\n### Setup Development Environment\n\n```bash\n# Clone repository\ngit clone https://github.com/AceDataCloud/SoraMCP.git\ncd SoraMCP\n\n# Create virtual environment\npython -m venv .venv\nsource .venv/bin/activate  # or `.venv\\Scripts\\activate` on Windows\n\n# Install with dev dependencies\npip install -e \".[dev,test]\"\n```\n\n### Run Tests\n\n```bash\n# Run unit tests\npytest\n\n# Run with coverage\npytest --cov=core --cov=tools\n\n# Run integration tests (requires API token)\npytest tests/test_integration.py -m integration\n```\n\n### Code Quality\n\n```bash\n# Format code\nruff format .\n\n# Lint code\nruff check .\n\n# Type check\nmypy core tools\n```\n\n### Build & Publish\n\n```bash\n# Install build dependencies\npip install -e \".[release]\"\n\n# Build package\npython -m build\n\n# Upload to PyPI\ntwine upload dist/*\n```\n\n## Project Structure\n\n```\nSoraMCP/\n├── core/                   # Core modules\n│   ├── __init__.py\n│   ├── client.py          # HTTP client for Sora API\n│   ├── config.py          # Configuration management\n│   ├── exceptions.py      # Custom exceptions\n│   ├── server.py          # MCP server initialization\n│   ├── types.py           # Type definitions\n│   └── utils.py           # Utility functions\n├── tools/                  # MCP tool definitions\n│   ├── __init__.py\n│   ├── video_tools.py     # Video generation tools\n│   ├── task_tools.py      # Task query tools\n│   └── info_tools.py      # Information tools\n├── prompts/                # MCP prompt templates\n│   └── __init__.py\n├── tests/                  # Test suite\n│   ├── conftest.py\n│   ├── test_client.py\n│   ├── test_config.py\n│   ├── test_integration.py\n│   └── test_utils.py\n├── deploy/                 # Deployment configs\n│   └── production/\n│       ├── deployment.yaml\n│       ├── ingress.yaml\n│       └── service.yaml\n├── .env.example           # Environment template\n├── .gitignore\n├── CHANGELOG.md\n├── Dockerfile             # Docker image for HTTP mode\n├── docker-compose.yaml    # Docker Compose config\n├── LICENSE\n├── main.py                # Entry point\n├── pyproject.toml         # Project configuration\n└── README.md\n```\n\n## Contributing\n\nContributions are welcome! Please:\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing`)\n3. Commit your changes (`git commit -m 'Add amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing`)\n5. Open a Pull Request\n\n## License\n\nMIT License - see [LICENSE](LICENSE) for details.\n\n## Links\n\n- [AceDataCloud Platform](https://platform.acedata.cloud)\n- [Sora Official](https://openai.com/sora)\n- [Model Context Protocol](https://modelcontextprotocol.io)\n- [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)\n\n---\n\nMade with love by [AceDataCloud](https://platform.acedata.cloud)\n",
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