MCP For PLCnext Control Opens Automation Data To AI Agents
Phoenix Contact introduces MCP for PLCnext Control, an app that gives AI agents standardised, controlled access to automation data and functions via the Model Context Protocol.
www.phoenixcontact.com
Integrating artificial intelligence into industrial environments requires access to real machine data without compromising the security of the control system. With MCP for PLCnext Control, Phoenix Contact is expanding its industrial AI portfolio with a standardised interface between industrial automation and AI applications. The app provides information, diagnostic data and selected functions of systems based on PLCnext Technology via the open Model Context Protocol (MCP). It runs directly on the PLCnext Control and makes relevant automation context accessible to compatible AI applications and AI agents.
A Single Interface For Automation Context
AI can provide particular support during commissioning, diagnostics and maintenance, but it needs information from the actual automation system to do so. Instead of addressing the various PLCnext Technology services individually, MCP for PLCnext Control offers a common, standardised interface for retrieving key information. Depending on the PLCnext services available and the tools activated, this can include device and firmware information, runtime and status information, diagnostic data, files and log files, OPC UA data, as well as events, alarms and notifications.
Faster Troubleshooting With Human Decision-Making
The practical benefit is particularly evident in troubleshooting. When a fault occurs, relevant information often has to be gathered from different sources and evaluated. MCP for PLCnext Control provides the existing automation context in a structured form for connected AI applications, which can, for example, include process values as well as status and diagnostic information in their analysis. Other knowledge sources, such as documentation or service information, can be integrated separately within the higher-level AI solution. The AI thus receives not just isolated information but context from the real automation system, so possible causes of faults can be narrowed down more quickly and explanations and next steps prepared. Assessment and decision-making remain with the user. The solution can generally be coupled with MCP-compatible clients and AI agents.
For industrial use, the app also takes controlled access into account. Measures include authentication, role-based access control, secure communication, logging and request rate limiting, using existing PLCnext Technology security mechanisms. With MCP for PLCnext Control, Phoenix Contact creates an open link between agentic AI and industrial automation, allowing automation context to be used specifically for AI-supported engineering, diagnostic and service processes.
Additional Context
This section details technical specifications and competitive benchmarking not included in the original product announcement.
The integration of large language models and AI agents into industrial control environments represents a significant shift for operational technology networks. The Model Context Protocol is an emerging open standard that defines how AI models access contextual data and tools, reducing the need for custom middleware for each data source. Across the automation market, edge-based AI integration and secure semantic data models are becoming a priority. Running the protocol server directly on the controller keeps diagnostic data processing at machine level, which supports industrial cybersecurity frameworks such as the IEC 62443 series by limiting uncontrolled exposure of production data. Access control at controller level is also becoming an important benchmark for combining non-deterministic AI analysis with deterministic industrial control.
Edited by Maria Brueva, Induportals editor – adapted by AI.
