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Learn to build MCP (Model Context Protocol) clients using Spring AI to augment Large Language Models with custom context and functionality in this comprehensive tutorial. Discover how to create a Spring Boot application that acts as an MCP client, connecting to MCP servers to enhance AI applications with real-time, custom data that LLMs weren't originally trained on. Follow along as you build a practical example using the "Dan Vega as a Service" MCP server to demonstrate overcoming traditional LLM limitations. Master setting up an MCP client using Spring AI 1.1.0 milestone 3, configuring multiple transport types including Streamable HTTP, SSE, and STDIO for MCP servers. Explore integrating OpenAI or any LLM with custom MCP servers for enhanced context, building REST endpoints that leverage MCP tools for augmented AI responses, and implementing best practices for connecting multiple MCP servers in a single client. The tutorial covers project setup with Spring Initializr, configuring OpenAI API keys, setting up MCP client configuration in YAML, creating chat controllers, and testing MCP client integration. Understand how MCP clients provide a model-agnostic approach to augment LLMs with custom context and capabilities, allowing seamless switching between OpenAI, Anthropic, Google Gemini, or any supported LLM without code changes. Perfect for developers building AI-powered applications requiring access to proprietary data, real-time information, or custom functionality not available in pre-trained models.
