📦

Node Red Contrib

MCP (Model Context Protocol) nodes for Node-RED — connect AI agents to any MCP server

0 installs

Trust: 37 — Low

Agents

Ask AI about Node Red Contrib

I know everything about Node Red Contrib. Ask me about installation, configuration, usage, or troubleshooting.

0/500

Loading tools...

Reviews

Documentation

node-red-contrib-mcp

The bridge between Node-RED and AI agents

Install · Quick Start · Nodes · AI Agent · Examples

AI agent flow in Node-RED — manufacturing OEE deep analysis using MCP tools
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

Node	Description
mcp server	Config — MCP server connection (URL, transport, API key)
llm config	Config — LLM provider (base URL, model, API key)
mcp tool	Call any MCP tool. Pass arguments as `msg.payload`, tool name in config or `msg.topic`
mcp tools	List all available tools from an MCP server. Great for discovery and debugging
mcp resource	Read resources exposed by an MCP server
llm call	Call any OpenAI-compatible LLM. Supports system prompt, JSON mode, multi-turn chat
ai agent	Autonomous agent — LLM + MCP tools in a reasoning loop until it has an answer

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

Setting	Default	Description
MCP Server	—	Which MCP server to use for tools
LLM	—	Which LLM provider for reasoning
System Prompt	—	Agent personality and instructions
Max Loops	10	Maximum LLM ↔ tool iterations
Temperature	0.3	LLM creativity (0 = focused, 1 = creative)

Examples

Import this flow

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

Example: MCP Tool Call

[
  {
    "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"
  }
]

Example: AI Agent

[
  {
    "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"
  }
]

Example: MQTT → AI Agent → MQTT (IIoT)

[
  {
    "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
  }
]

MQTT alert comes in → AI agent investigates via MCP tools → action goes out via MQTT.

Compatible with

MCP Servers

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

OpenShopFloor — 111 manufacturing MCP tools (ERP, OEE, QMS, TMS, UNS, KG)
Anthropic MCP Servers — filesystem, GitHub, PostgreSQL, Slack, Google Drive, ...
Any custom MCP server you build

LLM Providers

Works with any OpenAI-compatible API:

Provider	Base URL
OpenAI	`https://api.openai.com/v1`
Ollama (local)	`http://localhost:11434/v1`
Azure OpenAI	`https://YOUR.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT/v1`
vLLM	`http://localhost:8000/v1`
LiteLLM	`http://localhost:4000/v1`
LM Studio	`http://localhost:1234/v1`
Anthropic	via LiteLLM proxy

msg Reference

mcp-tool-call

Direction	Property	Type	Description
Input	`msg.payload`	object	Tool arguments
Input	`msg.topic`	string	Tool name (if not set in config)
Output	`msg.payload`	any	Tool result (auto-parsed JSON)
Output	`msg.mcpResult`	object	Raw MCP response

ai-agent

Direction	Property	Type	Description
Input	`msg.payload`	string	User question or task
Output	`msg.payload`	string	Agent's final answer
Output	`msg.agentLog`	array	`[{tool, args, result}]` for each call
Output	`msg.iterations`	number	Total LLM reasoning steps

llm-call

Direction	Property	Type	Description
Input	`msg.payload`	string	User message
Input	`msg.messages`	array	Previous conversation (multi-turn)
Input	`msg.tools`	array	OpenAI-format tool definitions
Output	`msg.payload`	string	LLM response text
Output	`msg.toolCalls`	array	Tool calls (if any)
Output	`msg.usage`	object	Token usage stats

Configuration

MCP Server (config node)

Field	Description	Example
URL	MCP server endpoint	`http://localhost:3001/mcp`
Transport	Protocol variant	`Streamable HTTP` (default) or `SSE`
API Key	Optional Bearer token	`sk-...`

LLM Provider (config node)

Field	Description	Example
Base URL	OpenAI-compatible endpoint	`https://api.openai.com/v1`
Model	Model identifier	`gpt-4o`
API Key	Your API key	`sk-...`

Use Cases

Domain	What you can build
Manufacturing	OEE monitoring, capacity planning, predictive maintenance, quality root cause analysis
IIoT	MQTT → AI Agent → MQTT pipelines, sensor data analysis, anomaly detection
Building Automation	Smart energy management, BACnet/Modbus + AI reasoning
IT / DevOps	Database agents, log analysis, automated incident response
Prototyping	Fastest way to prototype agentic AI — visual debugging in Node-RED

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