Article Details

Abstract

With AI technology driving digital transformation across industries, large models serve as the cornerstone for practical applications due to their emergent capabilities and multimodal advantages. However, embedding them into business workflows faces the “last mile” challenge of “tool islands” and low integration efficiency. The MCP (Model Context Protocol) open protocol, introduced in 2024, offers an innovative solution to this pain point. This paper systematically outlines the MCP technical framework, analyzes its working principles, and validates its practical value through case studies. To demonstrate utility, it implements three typical scenarios including an MCP-based CAD drafting assistant, a multi-table and multi-file processing assistant, and an intelligent agent office platform. All three scenarios achieve over 20% efficiency improvement, confirming MCP's core advantages in versatility, scalability, and security. These findings provide theoretical support, design paradigms, and a replicable pathway for enterprises to build next-generation AI agents.

Keywords

AI; MCP; agent

References

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About the authors

Li Chenyang (1982-), male, Han ethnicity, born in Taiyuan, Shanxi Province, is a technical chief engineer with a bachelor's degree. His research interests include production testing software solutions and development, digitalization, and intelligent manufacturing; Wang Xiaolin (1980-), male, Han ethnicity, born in Huai'an, Jiangsu, is a minister with a bachelor's degree. His research interests include the design and development of production testing systems, intelligent manufacturing technology, and digital technology; Xu Xiaofeng (1985-), male, Han ethnicity, from Weinan, Shaanxi, is a department head and a master's student. His research interests include production testing software solutions and development, intelligent manufacturing technology, and digital technology; Han Feng (1983-), male, Han ethnicity, born in Xi'an, Shaanxi Province, is a department head with a bachelor's degree. His research interests include production testing software solutions and development, intelligent manufacturing technology, and digital technology; Kang Weidong (1987-), male, Han ethnicity, from Baoji, Shaanxi Province, is a department head with a bachelor's degree. His research interests include production testing system solutions and development, AI intelligent detection technology, and automated testing technology; Wang Fengwei (1982-), female, Han ethnicity, from Jining, Shandong, is a technical expert and master's student. Her research direction is terminal intelligent manufacturing technology and solutions; Cheng Yihui (1976-), male, Han ethnicity, from Wuhan, Hubei, is a technical expert with a bachelor's degree. His research interests include digital transformation application solutions and implementation in terminal factories, as well as intelligent manufacturing; Li Yue (1981-), male, Han ethnicity, Shanxi Datong, technical expert, master's student, research direction: production testing software solution and development, intelligent manufacturing technology, digital technology.