DolphinScheduler MCP Server

DolphinScheduler MCP Server

The DolphinScheduler MCP server turns the powerful workflow orchestration platform Apache DolphinScheduler into a first‑class AI tool. By exposing the entire REST API through the Model Context Protocol, it lets conversational agents—such as Claude or other LLMs—create, modify, and monitor complex data pipelines without writing any HTTP code. This abstraction is especially valuable for developers who want to embed intelligent automation into data‑engineering workflows or build chat‑based interfaces for non‑technical users.

At its core, the server implements a FastMCP runtime that translates MCP tool calls into DolphinScheduler API requests. Each tool corresponds to a logical unit of work: project management, process definition, task scheduling, resource handling, and more. The server presents these tools with standardized input schemas and response formats, so an AI assistant can discover and invoke them through a simple JSON payload. This tight integration removes the need for custom SDKs or manual API key handling, making it straightforward to plug the server into any MCP‑compliant client.

Key capabilities include:

• Full API coverage – Every endpoint that DolphinScheduler offers is available as an MCP tool, from project creation to tenant administration.
• Standardized interfaces – Tools follow the MCP specification, providing consistent parameter validation and error handling across all operations.
• Easy configuration – Environment variables control API URLs, authentication tokens, host/port bindings, and logging levels without code changes.
• Rich documentation – Each tool is accompanied by clear descriptions, making it simple for developers to understand available actions and required arguments.

Typical use cases span the data‑engineering spectrum. An AI assistant can schedule a nightly ETL job, pause or resume pipelines on demand, and automatically provision resources when a new tenant joins the platform. In educational settings, students can interact with DolphinScheduler through conversational prompts, learning workflow concepts without writing code. In DevOps scenarios, chatbots can monitor system health and trigger alerts by invoking the server’s monitoring tools.

Because the MCP layer decouples the AI model from the underlying REST API, developers can evolve DolphinScheduler independently while keeping a stable interface for assistants. The server’s standardized toolset also enables rapid prototyping of new workflows, as the same AI agent can switch between creating a project and scheduling a task with a single command. This seamless bridge makes the DolphinScheduler MCP server an essential component for any organization looking to embed intelligent orchestration into its AI‑powered toolchain.