Core Insights - The article discusses the rapid development of AI, particularly large language models, and the emergence of the Model Context Protocol (MCP) as a solution to integrate these models with external systems, enhancing their functionality and responsiveness [1][2]. Group 1: Importance of MCP - MCP serves as a critical solution to the challenges faced in integrating AI with real-time data sources, allowing models to access and utilize dynamic information rather than relying solely on static knowledge bases [2][3]. - The protocol enables AI to interact with various resources, including local files, APIs, and third-party tools, transforming AI from a "data island" into a connected intelligent hub [2][3]. Group 2: Development of MCP Server - Developing an MCP Server involves several stages, including environment preparation, core functionality development, and testing, with the overall timeline depending on the complexity of the features being implemented [5][6]. - The most challenging aspect of the development process is defining tools in a way that allows the language model to understand their semantics and usage scenarios, emphasizing the importance of clear documentation over mere code implementation [6][7]. Group 3: Compatibility and Adaptation - Compatibility issues can arise when integrating MCP Server with different AI models, particularly regarding parameter handling, which may require specific adaptations for models that do not support complex structures [9][10]. - Solutions for adaptation include parameter flattening, creating specific adapters, and employing fallback strategies to ensure compatibility across various models [10]. Group 4: Performance and Efficiency - To ensure timely data transmission and processing, especially in real-time applications, MCP Server utilizes techniques such as Server-Sent Events (SSE) and caching mechanisms to minimize latency [11][12]. - When connecting to legacy systems, strategies like persistent connection pools and preloading frequently accessed data can significantly reduce initial query delays [12]. Group 5: Advantages of MCP over Other Protocols - MCP's automatic service discovery feature significantly reduces the integration workload compared to OpenAI's function calling, potentially decreasing the effort by up to 80% when switching between multiple models [13].