OPC UA MCP Server

Overview

The OPC UA MCP Server is a bridge that lets AI assistants—such as Claude or other Model Context Protocol clients—talk directly to industrial automation systems that expose data via OPC UA. By exposing a small set of intuitive tools, the server allows natural‑language queries to read or write real‑time values from machines, sensors, and control devices. This eliminates the need for developers to write custom OPC UA clients or to embed low‑level communication code in their AI workflows.

At its core, the server performs four essential operations: read a single node, write to a single node, browse the node hierarchy, and their batch counterparts. Each operation is wrapped in an MCP tool with clearly documented parameters (e.g., and ) and return messages that are already formatted for conversational use. For example, a user can ask “What’s the value of node ns=2;i=2?” and receive a concise reply that includes both the node identifier and its current value. Likewise, “Set node ns=2;i=3 to 100.” triggers a write operation and returns a success confirmation. This level of abstraction lets developers focus on business logic rather than protocol details.

Key features that make this server valuable include:

• Real‑time data access – Reads return the most recent values from connected OPC UA servers, enabling live monitoring of process parameters.
• Bidirectional control – Write operations allow AI agents to actuate devices, adjust setpoints, or trigger alarms directly from a conversation.
• Batch processing – Multiple read/write tools let users fetch or update dozens of nodes in a single call, improving efficiency for large‑scale monitoring.
• Seamless MCP integration – The server is pre‑configured to work with popular MCP clients like Claude Desktop, so no custom client code is required.

Typical use cases span a wide range of industrial scenarios. In predictive maintenance, an AI assistant can poll vibration or temperature sensors and flag anomalies before a fault occurs. In process optimization, the assistant might read current throughput metrics, suggest parameter tweaks, and write new setpoints—all via natural language. Manufacturing execution systems can also benefit: an operator could ask for the status of a conveyor belt, receive real‑time feedback, and command a restart if needed.

What sets this MCP server apart is its minimal footprint combined with full OPC UA support. It requires only a Python 3.13+ runtime and an existing OPC UA server, whether a simulator or a production device. The tool set is deliberately small yet expressive enough to cover most monitoring and control tasks, making it easy for developers to adopt without steep learning curves. As industrial AI continues to grow, having a ready‑made MCP server that plugs into OPC UA infrastructures empowers teams to deliver smarter, more responsive automation solutions.