# IoT-MCP: Bridging LLMs and IoT Systems Through Model Context Protocol

arXiv: https://arxiv.org/abs/2510.01260 · HTML: https://mlsanity.com/paper/2510.01260/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs](https://mlsanity.com/paper/2304.08244/) (2023)
- [Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models](https://mlsanity.com/paper/2304.09842/) (2023)
- [MCP-RADAR: A Multi-Dimensional Benchmark for Evaluating Tool Use Capabilities in Large Language Models](https://mlsanity.com/paper/2505.16700/) (2025)

## Built on by (most cited first, 9 of 9)

- [Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP](https://mlsanity.com/paper/2602.11327/) (2026)
- [Don't believe everything you read: Understanding and Measuring MCP Behavior under Misleading Tool Descriptions](https://mlsanity.com/paper/2602.03580/) (2026)
- [Skilled AI Agents for Embedded and IoT Systems Development](https://mlsanity.com/paper/2603.19583/) (2026)
- [Agentic AI for Scalable and Robust Optical Systems Control](https://mlsanity.com/paper/2602.20144/) (2026)
- [Listening Alone, Understanding Together: Collaborative Context Recovery for Privacy-Aware AI](https://mlsanity.com/paper/2604.13348/) (2026)
- [Octopus Protocol: One-Shot Hardware Discovery and Control for AI Agents via Infrastructure-as-Prompts](https://mlsanity.com/paper/2605.09055/) (2026)
- [Grid-Orch: An LLM-Powered Orchestrator for Distribution Grid Simulation and Analytics](https://mlsanity.com/paper/2605.12728/) (2026)
- [Device Context Protocol: A Compact, Safety-First Architecture for LLM-Driven Control of Constrained Devices](https://mlsanity.com/paper/2605.26159/) (2026)
- [TrustShiftProbe: Characterizing, Benchmarking, and Defending Staged Trust Attacks on MCP Servers](https://mlsanity.com/paper/2608.23763/) (2026)

## Abstract (arXiv)

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.