Unveiling MCP: The New Paradigm for Model-Context Interaction
In this course, we embark on a journey to explore the revolutionary Model Context Protocol (MCP). MCP is rapidly emerging as a game-changer in the realm of artificial intelligence, reshaping how models interact with context.
The traditional approaches to model-context interaction often suffer from limitations in standardization, flexibility, and context-awareness. MCP addresses these challenges head-on, introducing a unified framework that enables seamless communication between models and the rich context in which they operate.
Throughout the course, we will dissect the core components of MCP, including its innovative request and response structures, metadata management, and context chain mechanisms. Participants will gain in-depth knowledge of how MCP standardizes the representation of context, from simple prompt units to complex multi-turn dialogue scenarios. This standardization not only simplifies the integration of diverse models but also enhances their ability to generate more accurate, context-sensitive outputs.
Moreover, we will explore real-world applications of MCP, such as in chatbots, document-understanding systems, and multi-model collaborative platforms. By the end of this course, attendees will have a comprehensive understanding of MCP's potential to transform AI-based applications, and be equipped with the skills to implement MCP in their own projects, opening the door to a new era of model-context interaction.
The traditional approaches to model-context interaction often suffer from limitations in standardization, flexibility, and context-awareness. MCP addresses these challenges head-on, introducing a unified framework that enables seamless communication between models and the rich context in which they operate.
Throughout the course, we will dissect the core components of MCP, including its innovative request and response structures, metadata management, and context chain mechanisms. Participants will gain in-depth knowledge of how MCP standardizes the representation of context, from simple prompt units to complex multi-turn dialogue scenarios. This standardization not only simplifies the integration of diverse models but also enhances their ability to generate more accurate, context-sensitive outputs.
Moreover, we will explore real-world applications of MCP, such as in chatbots, document-understanding systems, and multi-model collaborative platforms. By the end of this course, attendees will have a comprehensive understanding of MCP's potential to transform AI-based applications, and be equipped with the skills to implement MCP in their own projects, opening the door to a new era of model-context interaction.
Lecturer
Date
20th October, 2025 ~ 7th January, 2026
Location
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| Monday,Wednesday | 13:30 - 15:05 | A3-2-201 | Zoom 16 | 468 248 1222 | BIMSA |
Website
Video Public
Yes
Notes Public
Yes
Lecturer Intro
Congwei Song received the master degree in applied mathematics from the Institute of Science in Zhejiang University of Technology, and the Ph.D. degree in basic mathematics from the Department of Mathematics, Zhejiang University, worked in Zhijiang College of Zhejiang University of Technology as an assistant from 2014 to 2021, from 2021 on, worked in BIMSA as asistant researcher. His research interests include machine learning, as well as wavelet analysis and harmonic analysis.