zapcap-mcp-server

# ZapCap MCP Server ## Overview This is an unofficial MCP (Model Context Protocol) server implementation for ZapCap.ai - a video processing and caption generation service. The server provides tools for uploading videos, creating processing tasks, and monitoring their progress through the ZapCap API. ## Project Structure ``` zapcap-mcp-server/ ├── src/ │ └── server.py # Main MCP server implementation ├── main.py # Entry point (placeholder) ├── pyproject.toml # Project configuration and dependencies ├── README.md # Basic project documentation └── uv.lock # Lock file for uv package manager ``` ## Package Management This project uses **uv** as the package manager instead of pip. All dependencies are managed through `pyproject.toml` and locked in `uv.lock`. ## Architecture The server is built using the FastMCP framework and provides the following tools: ### Available Tools 1. **zapcap_mcp_upload_video** - Upload video files to ZapCap 2. **zapcap_mcp_upload_video_by_url** - Upload videos by URL 3. **zapcap_mcp_get_templates** - Get available processing templates 4. **zapcap_mcp_create_task** - Create video processing tasks with full customization 5. **zapcap_mcp_monitor_task** - Monitor task progress ### Dependencies - `mcp[cli]>=1.10.1` - MCP server framework - `httpx>=0.28.1` - HTTP client for API requests - `pydantic>=2.11.7` - Data validation and serialization ## Configuration The server requires the `ZAPCAP_API_KEY` environment variable to be set for authentication with the ZapCap API. ## API Integration The server integrates with the ZapCap API endpoints: - `https://api.zapcap.ai/videos` - Video upload - `https://api.zapcap.ai/videos/url` - Upload by URL - `https://api.zapcap.ai/videos/{video_id}/task` - Task creation - `https://api.zapcap.ai/videos/{video_id}/task/{task_id}` - Task monitoring ## Data Models The server defines several Pydantic models for request validation: - `UploadVideo` - File path for video upload - `UploadVideoByUrl` - URL for video upload - `CreateTask` - Task creation parameters with full customization options - `MonitorTask` - Task monitoring parameters ## Error Handling The server includes basic error handling with `response.raise_for_status()` for HTTP errors and environment variable validation for API keys. ## Testing Testing infrastructure needs to be implemented. Consider using: - pytest for test framework - httpx-mock or responses for mocking HTTP requests - pytest-asyncio for async testing if needed ## Development Commands Since this project uses uv: - `uv sync` - Install dependencies - `uv run python main.py` - Run the application - `uv add <package>` - Add new dependencies - `uv run pytest` - Run tests (when implemented)