Excalibur of the AI Era: MCP Client Software Illustrated by `Cherry studio`
演讲者
时间
2026年01月23日 16:30 至 17:30
地点
A3-3-301
线上
Zoom 815 762 8413
(BIMSA)
摘要
As large language models transition from passive conversational systems to active, tool-augmented agents, the role of client software becomes increasingly critical. The Model Context Protocol (MCP) introduces a standardized framework for connecting models with external tools, resources, and execution environments. Within this framework, **MCP clients** emerge as the practical interface through which users, models, and systems interact.
This report focuses on MCP client software (called **MCP host**), using **chat-box–style applications such as Cherry Studio** as primary examples, while also examining related systems including **LobeHub/LM Studio/Anything LLM**, and other MCP-capable or MCP-adjacent tools. Beyond chat interfaces, the report surveys MCP integration and client-like roles in **programming environments (Cursor, Zed)**, **knowledge and note-taking systems (Obsidian, OpenWork)**, **desktop automation tools (Raycast)**, and **next-generation terminals (Warp)**.
By analyzing these tools from a unified perspective, the report argues that MCP clients function as the “saber” of the AI era: a controlled yet powerful extension that transforms raw model intelligence into effective, safe, and user-directed action. The discussion highlights architectural patterns, interaction models, and emerging design principles that define MCP clients as a foundational layer in modern AI systems.
This report focuses on MCP client software (called **MCP host**), using **chat-box–style applications such as Cherry Studio** as primary examples, while also examining related systems including **LobeHub/LM Studio/Anything LLM**, and other MCP-capable or MCP-adjacent tools. Beyond chat interfaces, the report surveys MCP integration and client-like roles in **programming environments (Cursor, Zed)**, **knowledge and note-taking systems (Obsidian, OpenWork)**, **desktop automation tools (Raycast)**, and **next-generation terminals (Warp)**.
By analyzing these tools from a unified perspective, the report argues that MCP clients function as the “saber” of the AI era: a controlled yet powerful extension that transforms raw model intelligence into effective, safe, and user-directed action. The discussion highlights architectural patterns, interaction models, and emerging design principles that define MCP clients as a foundational layer in modern AI systems.
演讲者介绍
宋丛威于2011年在浙江工业大学取得应用数学硕士学位，于2014年在浙江大学数学系取得基础数学博士学位，2014-2021年在浙江工业大学之江学院任讲师，2021至今任BIMSA助理研究员。主要研究方向：小波分析，调和分析，机器学习。