IoT-MCP: Bridging LLMs and IoT Systems Through Model Context Protocol
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.
68dfaa7cdf49fb0df1e03eb7
Ningyuan Yang
68dfaa7cdf49fb0df1e03eb8
Guanliang Lyu
68dfaa7cdf49fb0df1e03eb9
Mingchen Ma
68dfaa7cdf49fb0df1e03eba
Yiyi Lu
68dfaa7cdf49fb0df1e03ebb
Yiming Li
68dfaa7cdf49fb0df1e03ebc
Zhihui Gao
68dfaa7cdf49fb0df1e03ebd
Hancheng Ye
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2025-10-16T10:38:55.400Z
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/avatars/8fd7a210f3cb0de1229b49a3cd1ad8a0.svg
Hancheng Ye
user
HankYe
68dfaa7cdf49fb0df1e03ebe
Jianyi Zhang
68dfaa7cdf49fb0df1e03ebf
Tingjun Chen
68dfaa7cdf49fb0df1e03ec0
Yiran Chen
IoT-MCP: Bridging LLMs and IoT Systems Through Model Context Protocol
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.
IoT-MCP, a framework using the Model Context Protocol, enables seamless communication between Large Language Models and IoT devices, achieving high task success rates and low resource usage.
Large Language Models Internet-of-Things Model Context Protocol edge-deployed servers IoT-MCP IoT-MCP Bench Basic Tasks Complex Tasks sensor types microcontroller units tool calls response time memory footprint