Build AI-Powered Apps with MCP Clients in Spring AI

Description

Learn how to build MCP (Model Context Protocol) clients using Spring AI to augment Large Language Models with custom context and functionality! In this tutorial, I'll show you how to create a Spring Boot application that acts as an MCP client, connecting to MCP servers to enhance your AI applications with real-time, custom data that LLMs weren't trained on. We'll build a practical example using my "Dan Vega as a Service" MCP server to demonstrate how you can overcome the limitations of traditional LLMs. 📚 What You'll Learn: ✅ How to set up an MCP client using Spring AI 1.1.0 milestone 3 ✅ Configuring multiple transport types (Streamable HTTP, SSE, STDIO) for MCP servers ✅ Integrating OpenAI (or any LLM) with custom MCP servers for enhanced context ✅ Building REST endpoints that leverage MCP tools for augmented AI responses ✅ Best practices for stitching together multiple MCP servers in a single client ⏱️ Timestamps: 00:00 Introduction to MCP Clients 02:15 Understanding MCP Server limitations and solutions 04:30 Spring AI Documentation walkthrough 06:00 Project setup with Spring Initializr 08:30 Configuring OpenAI API keys 10:45 Setting up MCP client configuration in YAML 13:00 Creating the Chat Controller 15:30 Testing the MCP client integration 17:45 Real-world use cases and next steps 🔗 Resources: GitHub Repository: https://github.com/danvega/dvaas-client Spring AI Documentation: https://docs.spring.io/spring-ai Dan Vega as a Service MCP Server: https://mcp.danvega.dev/mcp Previous MCP Server Tutorial: https://youtu.be/MarSC2dFA9g Blog Post: https://www.danvega.dev/blog/spring-ai-mcp-client 💡 Key Takeaways: This tutorial demonstrates how MCP clients provide a model-agnostic way to augment LLMs with custom context and capabilities. You can swap between OpenAI, Anthropic, Google Gemini, or any supported LLM without changing your code - the MCP server handles the context augmentation consistently across all providers. Perfect for developers building AI-powered applications that need access to proprietary data, real-time information, or custom functionality not available in pre-trained models. 👍 If you found this tutorial helpful, please give it a thumbs up and subscribe for more Spring AI content! 👋🏻Connect with me: Website: https://www.danvega.dev Twitter: https://twitter.com/therealdanvega Github: https://github.com/danvega LinkedIn: https://www.linkedin.com/in/danvega Newsletter: https://www.danvega.dev/newsletter SUBSCRIBE TO MY CHANNEL: http://bit.ly/2re4GH0 ❤️