Sung Tae Yoo  Hyunsook Roh  Jae-Min Lee

10.5626/JOK.2025.52.11.984

local MCP AI agent molecular structure recognition SMILES conversion

With the development of large language models (LLMs), interest in artificial intelligence (AI) agents that automate various tasks has grown. The Model Context Protocol (MCP) enables standardized interactions between LLMs and tools, significantly enhancing the usability of AI agents. However, the integration of MCP with external tools is susceptible to various security threats, and there are limitations in ensuring system reliability and safety. In this paper, we propose a local MCP-based agent system that implements the host, client, and server structures of MCP within a local environment to address these issues. By managing tool invocation, execution, and response generation entirely on the local system, the proposed architecture reduces potential security vulnerabilities. Specifically, the system's effectiveness was experimentally validated through a process that recognizes molecular structures from images in research papers, automatically converts them into Simplified Molecular Input Line Entry System (SMILES) format, and verifies the results. Each step was executed using the ReAct method, where the LLMs alternate between reasoning and tool invocation, leveraging the outcomes of each action to inform the next step. This system demonstrates the potential for safely and flexibly operating AI agents in defense, medical, and industrial sectors based on local environments.