IndustriConnect: MCP Adapters and Mock-First Evaluation for AI-Assisted Industrial Operations

TL;DR

This paper introduces INDUSTRICONNECT, a suite of MCP adapters enabling AI assistants to interact with industrial protocols safely and effectively, validated through extensive benchmarking across various operational scenarios.

Contribution

It presents a novel framework of MCP adapters with mock-first evaluation for AI in industrial settings, ensuring protocol compatibility, safety, and robustness.

Findings

Full success in normal operation scenarios

Effective structured error handling validated

Concurrency boundaries identified and tested

Abstract

AI assistants can decompose multi-step workflows, but they do not natively speak industrial protocols such as Modbus, MQTT/Sparkplug B, or OPC UA, so this paper presents INDUSTRICONNECT, a prototype suite of Model Context Protocol (MCP) adapters that expose industrial operations as schema-discoverable AI tools while preserving protocol-specific connectivity and safety controls; the system uses a common response envelope and a mock-first workflow so adapter behavior can be exercised locally before connecting to plant equipment, and a deterministic benchmark covering normal, fault-injected, stress, and recovery scenarios evaluates the flagship adapters, comprising 870 runs (480 normal, 210 fault-injected, 120 stress, 60 recovery trials) and 2820 tool calls across 7 fault scenarios and 12 stress scenarios, where the normal suite achieved full success, the fault suite confirmed structured error handling with adapter-level uint16 range validation, the stress suite identified concurrency boundaries, and same-session recovery after endpoint restart is demonstrated for all three protocols, with results providing evidence spanning adapter correctness, concurrency behavior, and structured error handling for AI-assisted industrial operations.