Natural language is changing the way we interact with data—and Apache SeaTunnel is keeping up with the trend. Meet SeaTunnel MCP (Model Context Protocol): a new way to run data integration tasks using just plain English. In this article, we’ll walk you through what MCP is, why it matters, and how it connects large language models (LLMs) like Claude with the powerful SeaTunnel engine. Whether you're a data engineer, AI enthusiast, or just curious about the future of ETL, this is a project you’ll want to keep an eye on. What Is MCP? Why Propose MCP? In the current wave of large models rapidly permeating various scenarios, “natural language-operated data systems” are becoming a mainstream trend. MCP (Model Context Protocol) is a general solution proposed in this context to serve as a bridge connecting large language models (LLMs) with complex backend systems. More specifically, MCP Server is a server based on the MCP protocol designed to provide seamless integration between large language models and external data sources or tools. By standardizing the way AI systems interact with data sources, it helps models acquire richer contextual information, enabling them to generate more accurate and relevant responses. SeaTunnel MCP is a typical implementation of this protocol. Its goal is to allow users to efficiently perform data integration tasks—submission, management, and monitoring—on Apache SeaTunnel using natural language, thereby completely lowering the threshold for data processing. As an intelligent bridge connecting AI programming tools and SeaTunnel, SeaTunnel MCP Server enables developers to complete the following tasks through an AI assistant: implement interface calls to RESTful API V2 based on interactions with users. As for what more powerful things AI can do using this API documentation, it’s up to you and your team’s imagination. Goals and Use Cases of SeaTunnel MCP As a middle layer between LLMs and the SeaTunnel REST API, the SeaTunnel MCP Server has the following functional goals: ✅ Submit tasks via natural language interaction: Users can submit tasks directly through LLMs like Claude without understanding the underlying APIs;
✅ Monitor and manage job running status: Supports retrieving system health info and job metrics;
✅ Unified connection management: Simplifies connection configuration across multiple environments and instances;
✅ Automated orchestration of complex operations: Translates user intent into API call chains to automate task orchestration. ✅ Submit tasks via natural language interaction: Users can submit tasks directly through LLMs like Claude without understanding the underlying APIs; Submit tasks via natural language interaction ✅ Monitor and manage job running status: Supports retrieving system health info and job metrics; Monitor and manage job running status ✅ Unified connection management: Simplifies connection configuration across multiple environments and instances; Unified connection management ✅ Automated orchestration of complex operations: Translates user intent into API call chains to automate task orchestration. Automated orchestration of complex operations This design greatly expands SeaTunnel’s applicability in low-code/no-code scenarios. Full System Architecture of SeaTunnel MCP The overall interaction process of SeaTunnel MCP is as follows: The user interacts with Claude or other LLMs via natural language;
The LLM translates the intent into an MCP request (conforming to the Model Context Protocol);
The MCP Server receives the request and converts it into corresponding API calls;
SeaTunnel Client initiates an HTTP request to call the SeaTunnel REST API;
The SeaTunnel engine performs the actual operations;
Execution results are returned in reverse order, and finally, the LLM generates natural language feedback to the user. The user interacts with Claude or other LLMs via natural language; The LLM translates the intent into an MCP request (conforming to the Model Context Protocol); The MCP Server receives the request and converts it into corresponding API calls; SeaTunnel Client initiates an HTTP request to call the SeaTunnel REST API; The SeaTunnel engine performs the actual operations; Execution results are returned in reverse order, and finally, the LLM generates natural language feedback to the user. This architecture realizes a closed-loop transformation from "conversation understanding" to "system execution". Core Component Analysis of SeaTunnel MCP To support the above capabilities, the ST MCP architecture introduces the following key components: 1️⃣ FastMCP Server The core service component implementing the Model Context Protocol, serving as the entry point for LLM interaction. 2️⃣ SeaTunnel Client A communication wrapper for the SeaTunnel REST API, handling underlying details like authentication and data formats. 3️⃣ MCP Tools A set of functionally categorized tool libraries that encapsulate SeaTunnel client capabilities for the FastMCP Server to invoke. 4️⃣ CLI Toolchain A command-line interface for deploying, starting, and managing the MCP service. This component division ensures system scalability and modular deployment capability. Future Plans: Toward Greater Generalization Capabilities In SeaTunnel version 2.3.9 and later, MCP will support synchronization of all RESTful API V2 interfaces, further expanding its coverage. This means that in the future, you will be able to: ✨ Use natural language to complete full-link data task orchestration;
✨ One-click construct, monitor, and trace back complex data tasks;
✨ Quickly connect with more AI large model service providers. ✨ Use natural language to complete full-link data task orchestration; ✨ One-click construct, monitor, and trace back complex data tasks; ✨ Quickly connect with more AI large model service providers. Final Thoughts As LLM capabilities continue to grow, the “Natural Language × Data Integration” paradigm is accelerating the transformation of traditional ETL development models. The launch of SeaTunnel MCP is Apache SeaTunnel’s proactive exploration under this trend. If you're interested in SeaTunnel MCP, feel free to visit the open-source project page to participate in discussions and contribute to the community👇. This powerful service is looking forward to your brilliant ideas! 🔗 https://github.com/ocean-zhc/seatunnel-mcp https://github.com/ocean-zhc/seatunnel-mcp 📌 Related References: 📌 Related References: Apache SeaTunnel Official Website
What is the MCP Protocol? Apache SeaTunnel Official Website Apache SeaTunnel Official Website What is the MCP Protocol? What is the MCP Protocol?

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Apache SeaTunnel Launches MCP to Let You Run ETL Jobs with Natural Language

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