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Dolphinscheduler Mcp
What is Dolphinscheduler Mcp
DolphinScheduler MCP is a Model Context Protocol (MCP) server designed for Apache DolphinScheduler, enabling AI agents to interact with the DolphinScheduler RESTful API V1 and its ecosystem.
Use cases
Use cases include project management, process definition management, task scheduling, resource allocation, and system status monitoring.
How to use
To use DolphinScheduler MCP, install it via pip with ‘pip install dolphinscheduler-mcp’, then configure the server using environment variables or command-line arguments. Start the server with ‘ds-mcp --host 0.0.0.0 --port 8089’ or through the Python API.
Key features
Key features include full API coverage of DolphinScheduler, standardized tool interfaces, easy configuration, and comprehensive tool documentation.
Where to use
DolphinScheduler MCP can be used in fields requiring AI-driven workflow management, such as data processing, automated task scheduling, and resource management.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Dolphinscheduler Mcp
DolphinScheduler MCP is a Model Context Protocol (MCP) server designed for Apache DolphinScheduler, enabling AI agents to interact with the DolphinScheduler RESTful API V1 and its ecosystem.
Use cases
Use cases include project management, process definition management, task scheduling, resource allocation, and system status monitoring.
How to use
To use DolphinScheduler MCP, install it via pip with ‘pip install dolphinscheduler-mcp’, then configure the server using environment variables or command-line arguments. Start the server with ‘ds-mcp --host 0.0.0.0 --port 8089’ or through the Python API.
Key features
Key features include full API coverage of DolphinScheduler, standardized tool interfaces, easy configuration, and comprehensive tool documentation.
Where to use
DolphinScheduler MCP can be used in fields requiring AI-driven workflow management, such as data processing, automated task scheduling, and resource management.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
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 APIDOLPHINSCHEDULER_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
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.










