Feb 23, 2026
What is Model Context Protocol (MCP) in Manufacturing?
Every factory wants AI, but nobody wants ChatGPT touching their PLCs. Learn how the Model Context Protocol (MCP) provides secure, read-only AI access to industrial data.
The AI Dilemma in Manufacturing
The industrial sector is caught in a paradox. On one side, executives and engineers are eager to leverage Large Language Models (LLMs) like ChatGPT, Claude, and Gemini to analyze production bottlenecks, triage maintenance alerts, and generate shift handover reports. On the other side, OT (Operational Technology) and Cyber Security teams share a collective nightmare: What if a hallucinating AI accidentally writes a command to a PLC and stops the entire production line?
In manufacturing, AI cannot be given unfettered access to control systems. The risks of unexpected setpoint changes, safety interlock overrides, or catastrophic downtime are simply too great.
This is where the Model Context Protocol (MCP) enters the picture, bridging the gap between cutting-edge AI assistants and secure, mission-critical industrial networks.
What is Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open standard designed to connect AI models securely to external data sources and tools. Rather than trying to cram all of your company's proprietary data into an LLM's prompt window or giving raw database passwords to an AI, MCP acts as a secure, standardized middleman.
In a standard IT environment, an MCP server might allow an AI assistant to securely read your Slack messages or GitHub repositories. In Manufacturing, an MCP Server securely exposes machine telemetry, Unified Namespace (UNS) topics, and maintenance logs to the AI—without ever exposing the underlying PLCs, SCADA systems, or databases directly.
Why Manufacturing Needs MCP
Integrating AI into the shop floor using legacy methods usually involves brittle custom API scripts or risky direct database connections. MCP solves three fundamental industrial pain points:
1. The "Read-Only" Safety Boundary
AI models act as clients. They ask the MCP Server for specific data (e.g., "What is the current temperature of Oven 3?"). The MCP Server executes the query, fetches the data, and returns the result to the AI. Because the MCP Server handles the actual data extraction, you can mathematically guarantee that the AI is restricted to Read-Only operations. There is no physical or software path for the AI to execute a "Write" command to a machine.
2. Guardrails and Scoping (Governance)
With an MCP Server, you define the exact scope of what the AI is allowed to see. You can expose the last 7 days of OEE data and error logs, while completely hiding sensitive financial data or critical control tags. The AI doesn't get raw SQL access; it gets access to governed, pre-approved "Tools".
3. Full Auditability
Every time the AI requests data, the MCP server logs it. You retain a complete audit trail of exactly what the AI looked at, when it looked at it, and who initiated the prompt.
How Proxus Secures AI with the Proxus MCP Server
At Proxus, we identified that the future of Industrial IoT isn't just dashboards; it is conversational intelligence. However, we built the Proxus Platform with a strictly hardened architecture.
The Proxus MCP Server is a dedicated architectural component built specifically for industrial enterprise environments:
- OData Proxy and Schema Discovery: The Proxus MCP Server automatically translates your factory's Unified Namespace (Assets, Sites, Equipment) into an OData format that AI assistants naturally understand. The AI can discover your factory's layout dynamically.
- Secure Telemetry Queries: When an engineer asks an AI to "analyze the vibration anomalies on CNC Machine #4", the Proxus MCP Server executes a highly-optimized, read-only query against the underlying ClickHouse telemetry database. It protects against "Prompt Injection" by ensuring the AI cannot execute arbitrary SQL.
- Authentication & JWT: The MCP server doesn't blindly trust requests. It integrates directly with Proxus' authentication layer, ensuring that the human prompting the AI actually has the authorization to view that specific factory line's data.
Remember: The Proxus MCP Server sits on the IT layer. It communicates with the Edge Gateways via outbound-only MQTT streams. Even if an AI assistant goes completely rogue, it physically cannot reach down into the OT network to manipulate a PLC.
Real-World MCP Use Cases in the Factory
By standardizing on MCP, you unlock incredibly powerful workflows without compromising security:
- The 3 AM Maintenance Triage: A machine faults in the middle of the night. The on-duty technician asks their corporate AI assistant, "Summarize all critical alarms for Line 2 in the last 4 hours and compare them to known error codes." The AI securely queries the Proxus MCP Server, parses the telemetry, and provides a root-cause hypothesis instantly.
- Shift Handover Automation: Instead of spending 45 minutes writing shift reports, a production manager prompts the AI: "Generate a shift handover report. Include total throughput, any OEE dips below 70%, and outstanding maintenance tickets for the stamping press."
- Energy Optimization: "Cross-reference our real-time energy consumption (kWh) over the weekend with the production schedule. Were any high-draw machines left idling?"
Conclusion
The era of choosing between "Agile AI Innovation" and "Industrial OT Security" is over.
The Model Context Protocol (MCP) provides the exact framework needed to bring the power of LLMs to the factory floor safely. By utilizing a governed middle-layer like the Proxus MCP Server, manufacturers can give their engineering and operations teams a conversational interface to their industrial data, knowing with absolute certainty that their strictly read-only control layer remains untouchable.