IoT-MCP: Bridging LLMs and IoT Systems Through Model Context Protocol
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916984789139456 |
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| author | Yang, Ningyuan Lyu, Guanliang Ma, Mingchen Lu, Yiyi Li, Yiming Gao, Zhihui Ye, Hancheng Zhang, Jianyi Chen, Tingjun Chen, Yiran |
| author_facet | Yang, Ningyuan Lyu, Guanliang Ma, Mingchen Lu, Yiyi Li, Yiming Gao, Zhihui Ye, Hancheng Zhang, Jianyi Chen, Tingjun Chen, Yiran |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01260 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IoT-MCP: Bridging LLMs and IoT Systems Through Model Context Protocol Yang, Ningyuan Lyu, Guanliang Ma, Mingchen Lu, Yiyi Li, Yiming Gao, Zhihui Ye, Hancheng Zhang, Jianyi Chen, Tingjun Chen, Yiran Distributed, Parallel, and Cluster Computing Artificial Intelligence 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. |
| title | IoT-MCP: Bridging LLMs and IoT Systems Through Model Context Protocol |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2510.01260 |