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

主控程序 （模型上下文协议）是Anthropic为将人工智能连接到外部工具和数据而制定的开放标准。 此包将MCP带到Node-RED --全球最受欢迎的工业自动化和物联网低代码平台。

4M+节点RED安装 遇见 10000多台MCP服务器。 直观地构建AI代理。无需代码。

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特性

• 任何MCP服务器 --可流式传输HTTP和SSE，带可选身份验证
• 任何 LLM -OpenAI、Anthropic、Ollama、vLLM、Azure、Gemini或任何与OpenAI兼容的API
• AI代理节点 --全代理循环（工具发现→ LLM推理→ 工具执行→ 重复）
• 零锁定 --Apache-2.0许可证，无云依赖，完全本地运行
• 生产就绪 --错误处理、状态指示器、可配置超时
• 节点RED原生 --配置节点、消息传递、调试面板集成

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建筑

┌─────────────────────────────────────────────────────────────────┐
│  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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安装

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

或搜索 node-red-contrib-mcp 在调色板管理器中：

Menu → 管理调色板→ 安装→ node-red-contrib-mcp

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节点

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快速开始

1.调用MCP工具

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

2.LLM+MCP管道

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

3.AI Agent（魔法节点）

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

代理人 自主地 发现工具、调用原因，执行它们，并综合最终答案。与ChatGPT或Claude的模式相同，但具有可视性、可审计性和Node-RED。

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AI 代理

这 ai agent node运行一个完整的代理推理循环：

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

代理设置

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例子

导入此流

复制下面的JSON，然后在Node-RED中： Menu → 导入→ 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警报进入→ AI代理通过MCP工具进行调查→ 操作通过MQTT发出。*

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兼容

MCP服务器

使用 任何MCP服务器 支持流式HTTP或SSE传输：

• OpenShopFloor --111种制造MCP工具（ERP、OEE、QMS、TMS、UNS、KG）
• Anthropic MCP服务器 --文件系统、GitHub、PostgreSQL、Slack、谷歌云端硬盘。..
• 您构建的任何自定义MCP服务器

大语言模型提供商

使用 任何与OpenAI兼容的API:

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msg参考

mcp工具调用

人工智能代理

llm呼叫

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配置

MCP服务器（配置节点）

LLM提供程序（配置节点）

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用例

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需求

• 节点红色>=3.0.0
• Node.js>=18.0.0
• 要连接的MCP服务器
• LLM API密钥（用于 llm-call 和 ai-agent 节点）

贡献

问题和PR欢迎！

许可证

阿帕奇-2.0 --无论在商业上还是非商业上，都可以在任何地方使用它。

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

Live Demo · GitHub · Node-RED Flow Library