IoT-MCP: Bridging LLMs and IoT Systems Through Model Context Protocol
Authors: name: Ningyuan Yang, name: Guanliang Lyu, name: Mingchen Ma, name: Yiyi Lu, name: Yiming Li, name: Zhihui Gao, name: Hancheng Ye, name: Jianyi Zhang, name: Tingjun Chen, name: Yiran Chen

The integration of Large Language Models (LLMs) with Internet-of-Things (IoT)
systems faces significant challenges in hardware heterogeneity and control
complexity. The Model Context Protocol (MCP) emerges as a critical enabler,
providing standardized communication between LLMs and physical devices. We
propose IoT-MCP, a novel framework that implements MCP through edge-deployed
servers to bridge LLMs and IoT ecosystems. To support rigorous evaluation, we
introduce IoT-MCP Bench, the first benchmark containing 114 Basic Tasks (e.g.,
``What is the current temperature?'') and 1,140 Complex Tasks (e.g., ``I feel
so hot, do you have any ideas?'') for IoT-enabled LLMs. Experimental validation
across 22 sensor types and 6 microcontroller units demonstrates IoT-MCP's 100%
task success rate to generate tool calls that fully meet expectations and
obtain completely accurate results, 205ms average response time, and 74KB peak
memory footprint. This work delivers both an open-source integration framework
(https://github.com/Duke-CEI-Center/IoT-MCP-Servers) and a standardized
evaluation methodology for LLM-IoT systems.