Node Red Contrib Mcp

node-red-contrib-mcp The bridge between Node-RED and AI agents

Install · Quick Start · Nodes · AI Agent · Examples

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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.

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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

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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) │
    └──────────────┘                      └──────────────┘

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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

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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 |

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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.

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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) |

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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.

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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 |

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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 |

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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-... |

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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 |

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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](LICENSE) — use it anywhere, commercially or not.

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

Live Demo · GitHub · Node-RED Flow Library

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: TobiasLante
• Source: TobiasLante/node-red-contrib-mcp
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.