dolphinscheduler-mcp

# DolphinScheduler MCP Server A Model Context Protocol (MCP) server for Apache DolphinScheduler, allowing AI agents to interact with DolphinScheduler through a standardized protocol. ## Overview DolphinScheduler MCP provides a FastMCP-based server that exposes DolphinScheduler's REST API as a collection of tools that can be used by AI agents. The server acts as a bridge between AI models and DolphinScheduler, enabling AI-driven workflow management. ## Features - Full API coverage of DolphinScheduler functionality - Standardized tool interfaces following the Model Context Protocol - Easy configuration through environment variables or command-line arguments - Comprehensive tool documentation ## Installation ```bash pip install dolphinscheduler-mcp ``` ## Configuration ### Environment Variables - `DOLPHINSCHEDULER_API_URL`: URL for the DolphinScheduler API (default: http://localhost:12345/dolphinscheduler) - `DOLPHINSCHEDULER_API_KEY`: API token for authentication with the DolphinScheduler API - `DOLPHINSCHEDULER_MCP_HOST`: Host to bind the MCP server (default: 0.0.0.0) - `DOLPHINSCHEDULER_MCP_PORT`: Port to bind the MCP server (default: 8089) - `DOLPHINSCHEDULER_MCP_LOG_LEVEL`: Logging level (default: INFO) ## Usage ### Command Line Start the server using the command-line interface: ```bash ds-mcp --host 0.0.0.0 --port 8089 ``` ### Python API ```python from dolphinscheduler_mcp.server import run_server # Start the server run_server(host="0.0.0.0", port=8089) ``` ## Available Tools The DolphinScheduler MCP Server provides tools for: - Project Management - Process Definition Management - Process Instance Management - Task Definition Management - Scheduling Management - Resource Management - Data Source Management - Alert Group Management - Alert Plugin Management - Worker Group Management - Tenant Management - User Management - System Status Monitoring ## Example Client Usage ```python from mcp_client import MCPClient # Connect to the MCP server client = MCPClient("http://localhost:8089/mcp") # Get a list of projects response = await client.invoke_tool("get-project-list") # Create a new project response = await client.invoke_tool( "create-project", {"name": "My AI Project", "description": "Project created by AI"} ) ``` ## License Apache License 2.0