# node-red-contrib-mcp: Visually Building AI Agent Workflows in Node-RED

> Introduce Anthropic's MCP (Model Context Protocol) into Node-RED, enabling industrial automation and IoT developers to connect AI agents with external tools via drag-and-drop, achieving code-free intelligent workflow orchestration.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-06T17:15:07.000Z
- 最近活动: 2026-04-06T17:20:57.798Z
- 热度: 159.9
- 关键词: Node-RED, MCP, AI Agent, 工业自动化, 物联网, 低代码, LLM, 智能制造
- 页面链接: https://www.zingnex.cn/en/forum/thread/node-red-contrib-mcp-node-red-ai-agent
- Canonical: https://www.zingnex.cn/forum/thread/node-red-contrib-mcp-node-red-ai-agent
- Markdown 来源: floors_fallback

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## Introduction: Core Value of the node-red-contrib-mcp Project

# Introduction: Core Value of the node-red-contrib-mcp Project
The node-red-contrib-mcp project integrates Anthropic's MCP (Model Context Protocol) into the Node-RED low-code platform, allowing industrial automation and IoT developers to connect AI agents with external tools via drag-and-drop, realizing code-free intelligent workflow orchestration. This project links the Node-RED ecosystem (over 4 million installations) with MCP servers (over 10,000 available), bringing new possibilities for low-code AI automation in Industry 4.0, smart manufacturing, and IoT applications.

## Project Background and Overview

# Project Background and Overview
With the rapid development of Large Language Model (LLM) capabilities, how to seamlessly integrate AI into industrial automation and IoT systems has become a problem for engineers. Node-RED, as a globally popular low-code IoT development platform, has over 4 million installations and is widely used in industrial automation, smart home, and other fields. MCP is an open standard launched by Anthropic for connecting AI with external tools/data sources, with over 10,000 MCP servers available.
The node-red-contrib-mcp project combines the two, enabling developers to build AI Agent workflows in the Node-RED visual interface without code, empowering scenarios like Industry 4.0.

## Introduction to the MCP Protocol

# Introduction to the MCP Protocol
Model Context Protocol (MCP) is an open standard protocol that defines the communication method between AI models and external tools/data sources. Through MCP, AI agents can dynamically discover and call external functions (such as querying databases, reading files), and the standardized interface avoids writing separate integration code for each tool.
Traditional AI tool calls require hard-coding details, while MCP abstracts the process through a unified protocol, improving interoperability and development efficiency. node-red-contrib-mcp brings this capability into the Node-RED ecosystem.

## Core Features and Node Introduction

# Core Features and Node Introduction
The project provides a series of Node-RED nodes covering server configuration to AI Agent orchestration:
## Server and Configuration Nodes
- **mcp server**: Configure MCP server connections, supporting Streamable HTTP/SSE protocols and authentication;
- **llm config**: Configure LLM providers (OpenAI, Anthropic, Ollama, and other services compatible with the OpenAI API).
## Tool Call Nodes
- **mcp tool**: Call any MCP tool, with parameters passed via msg.payload;
- **mcp tools**: List available tools on the MCP server;
- **mcp resource**: Read resources exposed by the MCP server.
## LLM Call and AI Agent Nodes
- **llm call**: Call OpenAI-compatible LLMs, supporting system prompts, JSON mode, and multi-turn conversations;
- **ai agent**: Core node that implements the agent reasoning loop (analyze problem → call tool → get result → iterate → generate answer).

## Typical Application Scenarios

# Typical Application Scenarios
## Manufacturing OEE Monitoring
Build an intelligent agent to automatically answer the reasons for OEE decline: call the get_oee tool to obtain data, call the get_downtime_events tool to get downtime events, and conduct comprehensive analysis to draw conclusions (e.g., OEE decline caused by bearing failure, tool change delay, etc.).
## IIoT Intelligent Alarm Handling
Build an MQTT → AI Agent → MQTT pipeline: When a machine alarm occurs, the agent investigates the cause via MCP tools and recommends measures, then sends suggestions via MQTT to achieve unattended intervention.
## Smart Building and Energy Management
Combine with BACnet/Modbus protocol nodes, the AI agent analyzes energy usage patterns, automatically adjusts HVAC settings, optimizing energy consumption while maintaining comfort.

## Technical Features and Ecosystem Compatibility

# Technical Features and Ecosystem Compatibility
## Technical Features
- Openness and flexibility: No lock-in to LLM providers or MCP servers; free to choose;
- Open source and localization: Apache-2.0 license, no cloud dependency, supports local operation, meeting industrial data privacy requirements;
- Production-ready: Complete error handling, status indicators, timeout mechanisms, natively integrated with the Node-RED system.
## Compatibility
- MCP servers: Supports servers with Streamable HTTP/SSE protocols (e.g., OpenShopFloor, Anthropic official servers, custom servers);
- LLM support: Compatible with all OpenAI API services (OpenAI, Ollama, Azure OpenAI, etc.), Anthropic Claude can be called via LiteLLM proxy.

## Quick Start Guide

# Quick Start Guide
## Installation Methods
- Via npm: Execute `npm install node-red-contrib-mcp` in the Node-RED user directory;
- Via Palette Manager: Search for "node-red-contrib-mcp" in the Node-RED interface to install.
## Quick Experience
After installation, import the JSON examples provided by the project to quickly experience MCP tool calls and AI Agent functions.
## Documentation and Examples
The project provides detailed documentation and multiple examples (simple tool calls, complete AI Agent, MQTT-integrated IIoT, etc.) to help developers build custom workflows.

## Project Summary and Value

# Project Summary and Value
node-red-contrib-mcp brings AI agent capabilities to the industrial automation and IoT fields, allowing engineers to build intelligent applications in the Node-RED environment without deep knowledge of Python or AI frameworks. It connects 4 million Node-RED users with over 10,000 MCP servers, opening a new chapter in low-code AI automation, and is an extremely valuable tool for exploring AI applications in industrial scenarios.