node-red-contrib-mcp

The bridge between Node-RED and AI agents

Install · Quick Start · Nodes · AI Agent · Examples

Multi-phase OEE analysis agent built with MCP tool nodes in Node-RED

MCP (Model Context Protocol) is the open standard by Anthropic for connecting AI to external tools and data. This package brings MCP to Node-RED — the world's most popular low-code platform for industrial automation and IoT.

4M+ Node-RED installations meet 10,000+ MCP servers. Build AI agents visually. No code required.

Features

• Any MCP server — Streamable HTTP and SSE transport, with optional auth
• Any LLM — OpenAI, Anthropic, Ollama, vLLM, Azure, Gemini, or any OpenAI-compatible API
• AI Agent node — full agentic loop (tool discovery → LLM reasoning → tool execution → repeat)
• Zero lock-in — Apache-2.0 license, no cloud dependency, runs fully local
• Production-ready — error handling, status indicators, configurable timeouts
• Node-RED native — config nodes, msg passing, debug panel integration

Architecture

┌─────────────────────────────────────────────────────────────────┐
│  Node-RED                                                       │
│                                                                 │
│  [inject] → [mcp tool] → [llm call] → [mcp tool] → [debug]   │
│                                                                 │
│  [inject] → [ai agent] → [debug]    ← autonomous agent loop   │
│                                                                 │
└──────────┬──────────────────────────────────────┬───────────────┘
           │                                      │
           ▼                                      ▼
    ┌──────────────┐                      ┌──────────────┐
    │  MCP Server  │                      │   LLM API    │
    │  (any tool)  │                      │  (any model) │
    └──────────────┘                      └──────────────┘

Install

cd ~/.node-red
npm install node-red-contrib-mcp

Or search for node-red-contrib-mcp in the Palette Manager:

Menu → Manage palette → Install → node-red-contrib-mcp

Nodes

Quick Start

1. Call an MCP tool

[inject {"machine": "CNC-001"}] → [mcp tool "get_oee"] → [debug]

2. LLM + MCP pipeline

[inject] → [mcp tool "get_data"] → [llm call "Summarize this"] → [debug]

3. AI Agent (the magic node)

[inject "Why did OEE drop on machine 9014?"] → [ai agent] → [debug]

The agent autonomously discovers tools, reasons about which to call, executes them, and synthesizes a final answer. Same pattern as ChatGPT or Claude — but visual, auditable, and in your Node-RED.

AI Agent

The ai agent node runs a full agentic reasoning loop:

User: "Why did OEE drop on machine 9014 last week?"

  ┌─── Agent Loop ──────────────────────────────────────────┐
  │                                                         │
  │  Step 1: LLM sees 91 tools, picks get_oee              │
  │          → calls MCP server → gets OEE data             │
  │                                                         │
  │  Step 2: LLM analyzes, picks get_downtime_events        │
  │          → calls MCP server → gets 3 events             │
  │                                                         │
  │  Step 3: LLM synthesizes final answer                   │
  │                                                         │
  └─────────────────────────────────────────────────────────┘

Agent: "OEE dropped from 85% to 62% due to 3 unplanned stops:
        bearing failure (47min), tool change delay (23min),
        and material shortage (18min)."

  msg.agentLog = [{tool: "get_oee", ...}, {tool: "get_downtime_events", ...}]
  msg.iterations = 3

Agent settings

Examples

Import this flow

Copy the JSON below, then in Node-RED: Menu → Import → Paste

[
  {
    "id": "mcp-demo-inject",
    "type": "inject",
    "name": "Trigger",
    "props": [{ "p": "payload" }],
    "payload": "{\"machine_id\": \"CNC-001\"}",
    "payloadType": "json",
    "wires": [["mcp-demo-tool"]],
    "x": 150,
    "y": 100
  },
  {
    "id": "mcp-demo-tool",
    "type": "mcp-tool-call",
    "name": "Get OEE",
    "server": "mcp-demo-server",
    "toolName": "get_oee",
    "wires": [["mcp-demo-debug"]],
    "x": 350,
    "y": 100
  },
  {
    "id": "mcp-demo-debug",
    "type": "debug",
    "name": "Result",
    "active": true,
    "x": 550,
    "y": 100
  },
  {
    "id": "mcp-demo-server",
    "type": "mcp-server-config",
    "name": "My MCP Server",
    "url": "http://localhost:8021/mcp",
    "transportType": "http"
  }
]

[
  {
    "id": "agent-demo-inject",
    "type": "inject",
    "name": "Ask question",
    "props": [{ "p": "payload" }],
    "payload": "What is the current OEE of machine CNC-001 and what are the main loss factors?",
    "payloadType": "str",
    "wires": [["agent-demo-agent"]],
    "x": 170,
    "y": 100
  },
  {
    "id": "agent-demo-agent",
    "type": "ai-agent",
    "name": "Factory Agent",
    "server": "agent-demo-mcp",
    "llmConfig": "agent-demo-llm",
    "systemPrompt": "You are a manufacturing AI assistant. Use the available MCP tools to answer questions about factory operations. Be precise and cite specific numbers.",
    "maxIterations": 10,
    "temperature": 0.3,
    "maxTokens": 4096,
    "wires": [["agent-demo-debug"]],
    "x": 400,
    "y": 100
  },
  {
    "id": "agent-demo-debug",
    "type": "debug",
    "name": "Agent Response",
    "active": true,
    "x": 620,
    "y": 100
  },
  {
    "id": "agent-demo-mcp",
    "type": "mcp-server-config",
    "name": "Factory MCP",
    "url": "http://localhost:8024/mcp",
    "transportType": "http"
  },
  {
    "id": "agent-demo-llm",
    "type": "llm-config",
    "name": "OpenAI",
    "baseUrl": "https://api.openai.com/v1",
    "model": "gpt-4o"
  }
]

[
  {
    "id": "mqtt-in",
    "type": "mqtt in",
    "name": "machine/alerts",
    "topic": "machine/+/alert",
    "broker": "mqtt-broker",
    "wires": [["mqtt-agent"]],
    "x": 150,
    "y": 100
  },
  {
    "id": "mqtt-agent",
    "type": "ai-agent",
    "name": "Alert Agent",
    "server": "mqtt-mcp-server",
    "llmConfig": "mqtt-llm",
    "systemPrompt": "You are an industrial AI agent. When you receive a machine alert, investigate using MCP tools and recommend an action. Be concise.",
    "maxIterations": 5,
    "wires": [["mqtt-out"]],
    "x": 380,
    "y": 100
  },
  {
    "id": "mqtt-out",
    "type": "mqtt out",
    "name": "machine/actions",
    "topic": "machine/actions",
    "broker": "mqtt-broker",
    "x": 600,
    "y": 100
  }
]

Compatible with

MCP Servers

Works with any MCP server that supports Streamable HTTP or SSE transport:

• OpenShopFloor — 91 manufacturing MCP tools (ERP, OEE, QMS, WMS)
• Anthropic MCP Servers — filesystem, GitHub, PostgreSQL, Slack, Google Drive, ...
• Any custom MCP server you build

LLM Providers

Works with any OpenAI-compatible API:

msg Reference

mcp-tool-call

ai-agent

llm-call

Configuration

MCP Server (config node)

LLM Provider (config node)

Use Cases

Requirements

• Node-RED >= 3.0.0
• Node.js >= 18.0.0
• An MCP server to connect to
• An LLM API key (for llm-call and ai-agent nodes)

Contributing

Issues and PRs welcome! github.com/BavarianAnalyst/node-red-contrib-mcp

License

Apache-2.0 — use it anywhere, commercially or not.

Built by OpenShopFloor — the open-source AI platform for factory operations

Live Demo · GitHub · Node-RED Flow Library