MCP Introduction

MCP (Model Context Protocol) is currently a hot topic related to AI development. MCP provides a standard protocol to provide context data for AI models. MCP defines two roles: server and client. The server and the client interact with each other using a standard protocol. MCP server is responsible for providing knowledge, including prompt templates, resources, and tools. MCP client interacts with the server to obtain these knowledge, including creating prompts from templates, retrieving the content of resources, and invoking tools. A JSON-RPC based protocol is used between the server and the client. In this way, the provider of knowledge is decoupled from the consumer. The most important benefit of decoupling is to facilitate sharing and reuse.

For an AI application, only one MCP client needs to be integrated to use knowledge from different MCP servers. There is no restriction on how the MCP servers themselves are implemented. A Java-based AI application that integrates a Java MCP client can use knowledge from MCP servers written in JavaScript or Python.

The greatest value of MCP is that it facilitates the sharing of knowledge. A large number of reusable MCP servers can already be found publicly, see mcp.so. Due to the importance of MCP, the development of AI applications nowadays cannot be done without a good understanding of MCP.