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

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"}
)

License

Apache License 2.0