DolphinScheduler MCP Server

What is DolphinScheduler MCP Server?

DolphinScheduler MCP Server is a FastMCP-based server that exposes Apache DolphinScheduler’s REST API as standardized tools for AI agents. It acts as a bridge between AI models and DolphinScheduler, enabling AI-driven workflow management.

How to use DolphinScheduler MCP Server?

Install the Python package via pip install dolphinscheduler-mcp. Configure it using environment variables (e.g., DOLPHINSCHEDULER_API_URL, DOLPHINSCHEDULER_API_KEY) or command-line arguments. Start the server with the ds-mcp --host 0.0.0.0 --port 8089 command or by calling run_server() from the Python API. AI agents can then connect and invoke tools like get-project-list or create-project.

Key features of DolphinScheduler MCP Server

• Full coverage of DolphinScheduler REST API functionality
• Standardized tool interfaces following the Model Context Protocol
• Easy configuration via environment variables or CLI arguments
• Comprehensive tool documentation
• Tools for project, process, task, scheduling, resource, datasource, alert, worker, tenant, and user management
• System status monitoring support

Use cases of DolphinScheduler MCP Server

• AI agents list and manage DolphinScheduler projects
• AI-driven creation and modification of process definitions
• Automated scheduling and monitoring of workflow instances
• Intelligent resource and datasource management through natural language instructions
• Tenant, user, and alert group administration by AI assistants

FAQ from DolphinScheduler MCP Server

What problem does this server solve?

It provides a standardized MCP interface so AI agents can interact with Apache DolphinScheduler’s REST API without custom integration code.

What are the configuration requirements?

You must set DOLPHINSCHEDULER_API_URL (default http://localhost:12345/dolphinscheduler) and DOLPHINSCHEDULER_API_KEY for authentication. The server host, port, and log level can also be configured.

How do I start the server?

Run the ds-mcp command with optional --host and --port arguments, or use the run_server() function in Python.

What tools does the server expose?

Tools cover project, process definition, process instance, task definition, scheduling, resource, datasource, alert group, alert plugin, worker group, tenant, user management, and system status monitoring.

What runtime dependencies are needed?

Python 3.8+ and the dolphinscheduler-mcp package. The server connects to a running DolphinScheduler instance and uses the Model Context Protocol.