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iot-dc3

IoT DC3 — Connect the Physical World to AI. An open-source Industrial IoT Runtime for Physical AI: 36 protocol drivers, edge-to-cloud data delivery, and an MCP tool gateway so AI agents act on devices in a safe, controllable, traceable loop.

Install / Use

claude mcp add pnoker -- npx -y github:pnoker/iot-dc3

If the server publishes to npm under a different name, use that package instead — check the repo README.

About this skill
🔌

MCP Server

Model Context Protocol server

Quality Score

90/100

Supported Platforms

Claude Code
Claude Desktop

Tags

Our assessment of iot-dc3

iot-dc3 scores 90/100 on our quality scale, 1302nd of 4,597 Development & Engineering skills we index (top 29%).

Its MCP Server is 14 KB long, well organised into 16 sections with 1 code example: a thorough specification that gives an agent plenty to work with.

With 1,287 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
30/30
Structure
17/20
Description
15/15
Adoption
13/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated today, so iot-dc3 is actively maintained.
  • Our last check on 2026-09-24 found the source still online.
  • No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
  • Its trust signals score 88/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-24. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

iot-dc3 compared with similar skills

All 4 of these similar skills score higher than iot-dc3; compare them before choosing.

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Frequently asked questions

How do I install iot-dc3?
Run claude mcp add pnoker -- npx -y github:pnoker/iot-dc3. The install tabs above show the steps for each supported agent.
Which AI agents does iot-dc3 work with?
It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
Is iot-dc3 safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It declares no license and scores 88/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is iot-dc3 still maintained?
The repository was last updated today, so iot-dc3 is actively maintained.
<p align="right"> <a href="./README.md">English</a> | <a href="./README.zh.md">中文</a> | <a href="./README.ja.md">日本語</a> | <a href="./README.vi.md">Tiếng Việt</a> | <a href="./README.ko.md">한국어</a> | <a href="./README.es.md">Español</a> | <a href="./README.ru.md">Русский</a> </p>

AI assistants: Read README.ai.md first for a concise, AI-friendly overview of IoT DC3.

<p align="center"> <img src="./.github/brand/png/banner.en.png" alt="IoT DC3 — Connect the Physical World to AI · Open-source Industrial IoT Runtime for Physical AI"> </p> <p align="center"> <a href="https://github.com/pnoker/iot-dc3/stargazers"> <img src="https://img.shields.io/github/stars/pnoker/iot-dc3?style=flat&logo=github&color=green" alt="GitHub Stars"> </a> <a href="https://gitee.com/pnoker/iot-dc3/stargazers"> <img src="https://gitee.com/pnoker/iot-dc3/badge/star.svg?theme=gvp" alt="Gitee Star"> </a> <a href="https://gitee.com/pnoker/iot-dc3/members"> <img src="https://gitee.com/pnoker/iot-dc3/badge/fork.svg?theme=gvp" alt="Gitee Fork"> </a> <a href="https://github.com/pnoker/iot-dc3/graphs/contributors"> <img src="https://img.shields.io/github/contributors/pnoker/iot-dc3?label=contributors&color=orange" alt="Contributors"> </a> <img src="https://img.shields.io/badge/License-AGPL%203.0-blue" alt="License"> <img src="https://img.shields.io/badge/Java-21-orange?logo=openjdk" alt="Java 21"> <img src="https://img.shields.io/badge/Spring%20Boot-4.1-6DB33F?logo=springboot" alt="Spring Boot 4.1"> </p> <p align="center"> <strong> IoT DC3 — Connect the Physical World to AI<br> An open-source Industrial IoT Runtime for Physical AI </strong> </p> <p align="center"> <a href="https://dc3.site">https://dc3.site</a> </p> <p align="center"> 🔌 <strong>Multi-protocol connectivity</strong> &nbsp;·&nbsp; 🤖 <strong>MCP tool gateway</strong> &nbsp;·&nbsp; ☁️ <strong>Cloud-native microservices</strong> </p> <p align="center"> <em> IoT DC3 decouples <strong>devices</strong> from <strong>applications</strong>: drivers push normalized point data into a message bus, and applications consume it through one unified API — add or change devices without touching applications, and build new applications without touching devices. </em> </p>

📸 Product Preview

<table> <tr> <th width="33%">📸 Platform Overview</th> <th width="33%">📸 Device Management</th> <th width="33%">📸 AI Agent</th> </tr> <tr> <td align="center"> <img src="https://docs.dc3.site/images/screenshot-overview.png" alt="Platform dashboard" width="100%"> <br> <strong>Home / Dashboard</strong><br> <em>System overview · Online device metrics · Data trend charts</em> </td> <td align="center"> <img src="https://docs.dc3.site/images/screenshot-device.png" alt="Device management page" width="100%"> <br> <strong>Device Management</strong><br> <em>Device list · Online status · Search and filtering</em> </td> <td align="center"> <img src="https://docs.dc3.site/images/screenshot-ai.png" alt="AI agent assistant page" width="100%"> <br> <strong>AI Agent Assistant</strong><br> <em>Natural-language device queries · Data insights · Governed execution</em> </td> </tr> </table>

🏗️ Architecture Overview

Architecture at a Glance

IoT DC3 Architecture Panorama

Six-layer microservice architecture at a glance: clients → gateway → four center services → message bus → 36 protocol drivers → field devices. PostgreSQL (TimescaleDB + pgvector + AGE) persistence and optional observability stack (ELK + Prometheus + Grafana) laid out in one view.

🧱 Design principles — cross-service calls always go through Facade interfaces; the DO/BO/VO three-tier model keeps persistence, business, and API shapes strictly separated; and tenant isolation runs end to end across database, cache, and API paths. Clear boundaries that scale across services and teams.

📖 For the full architecture documentation, see System Architecture Overview.

✨ Core Features

🔌 Multi-Protocol Device Connectivity

IoT DC3 includes 36 access driver modules for industrial automation, IoT communication, data bridging, basic communication, and simulation/debugging scenarios, reducing the cost of connecting common devices and data sources:

| Category | Driver Modules | |-----------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 🏭 Industrial protocols | Modbus TCP · Modbus RTU · OPC UA · OPC DA · Siemens S7 · BACnet/IP · EtherNet/IP · Omron FINS · Mitsubishi MELSEC · IEC 60870-5-104 · IEC 61850 · DNP3 · DLMS · DLT645 · KNX · M-Bus · SL651 | | 📡 IoT protocols | MQTT · CoAP · LwM2M · HTTP · BLE · Zigbee · LoRaWAN | | 🗄️ Data bridging | MySQL · PostgreSQL · Oracle · SQL Server · Redis | | 🔧 Basic communication, messaging and NMS | TCP/UDP · Serial · SNMP · CAN · Kafka | | 🧪 Simulation and debugging | Virtual · Listening Virtual |

The Driver SDK supports fast development of custom protocol drivers and registration into the runtime platform.

🤖 From Device Data to Physical AI

The Agentic Center is built on Spring AI, and the platform exposes an MCP tool gateway so AI agents can act on the physical world in one safe, controllable, traceable loop:

  • MCP tool gateway — external AI agents connect through the Model Context Protocol: OAuth client registration, per-tool authorization, and a full audit trail for every tool call
  • Natural-language assisted operations - through Tool Calling and under access control, LLMs can query devices, read/write points, and assist with command execution
  • Intelligent alarm analysis - AI assists with root-cause analysis and response suggestions
  • Data insights - Query device data in natural language and generate visual charts
  • Multi-model support - Compatible with OpenAI API-style providers and mainstream models such as GPT, Claude, DeepSeek, and Qwen
  • Conversation memory - Multi-turn conversations and context memory persisted to the database

🏗️ Cloud-Native Microservices

Distributed microservice architecture based on Spring Boot 4 + Spring Cloud 2025:

  • Service governance - Spring Cloud Gateway as the unified entrypoint, with static routes and flexible environment variables
  • Efficient communication - gRPC service calls with Protobuf serialization
  • Horizontal scaling - Stateless design for scaling individual services by workload
  • Resilience - Replaceable service nodes and fault isolation

📊 Real-Time Data Engine

  • Data collection - Drivers collect device telemetry and send it asynchronously through the internal message broker — pluggable per deployment: RabbitMQ (default), Kafka, Pulsar or any MQTT 5 broker (broker guide)
  • Time-series storage - Efficient queries for real-time and historical data
  • Rule engine - Flexible alarm rules with multi-level alarms and notifications
  • Event traceability - Full command and event history

🔐 Enterprise Security and Multi-Tenancy

  • Tenant isolation - Tenant-level isolation across database, cache, and API paths
  • Authentication and authorization - JWT + Spring Security with RBAC
  • Transport encryption - TLS/SSL communication support
  • Audit tracking - User operation and system event logs

🧩 Developer Friendly

  • Driver SDK - A complete driver development toolkit. See the Driver Authoring Guide
  • Separated frontend and backend - Vue 3 + TypeScript frontend, RESTful and gRPC APIs
  • Containerized deployment - One-command startup with Podman / Docker Compose, plus compose scaling, Docker Swarm, Kubernetes and Helm deployment configs. See the Deployment Guide.
  • Complete documentation - Online docs, quickstart guide, and troubleshooting guide

⚡ Quick Start

For source-based local development, start PostgreSQL and RabbitMQ, load local environment variables, then build:

make up-db
source dc3/env/dev.env.sh
mvn -s .mvn/settings.xml clean package

Use make up-db-cn if you prefer the Alibaba Cloud registry in Mainland China.

📖 For service startup order, IDE setup, verification commands, and common pitfalls, see the full Quickstart.

🛠️ Technology Stack

IoT DC3 is built on Java 21, Spring Boot 4, Spring Cloud 2025, Spring AI 2, PostgreSQL, a pluggable message broker (RabbitMQ, Kafka, Pulsar or MQTT 5 — selection guide), gRPC, Vue 3, TypeScript, and Vite.

See Technology Stack for component details and where each technology is used.

📖 Documentation and Community

| Resource | Link | |-----------------------|---------------------------------------------------------------------------------| | 📚 Online docs | docs.dc3.site | | 🎬 Live demo | demo.dc3.site | | 🏭 Industry demos | dc3.site/en/demo | | 🚀 Quickstart | Quickstart Guide | | 🛠️ Technology stack | Technology Stack | | 🏗️ Architecture | Modules and Dependencies | | 🔧 Driver development | Driver Authoring Guide | | 🐛 Troubleshooting | Troubleshooting | | 📋 Changelog | Release Changelog | | 💰 Pricing & licensing | Plans and commercial license | | 🐛 Issue feedback | GitHub Issues | | 🇨🇳 Gitee mirror | Gitee GVP Project |

🌍 Use Cases

Twelve illustrative industry dashboards — built on IoT DC3 with mock data — show how the platform lands in each scenario. Browse all demos.

| | | | |--------------------|---------------------------------------------------------

Truncated for display — read the full file on GitHub.

Related Skills

View on GitHub
GitHub Stars1.3k
CategoryDevelopment
Updated12h ago
Forks233

Languages

Java

Trust signals

88/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

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