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.

Related MCP server: Role-Specific Context MCP Server

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

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:

ds-mcp --host 0.0.0.0 --port 8089

Python API

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

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"} )