From Prompts to Agents: Supercharging Spring Boot with AI and MCP

Thamaraikkanni Panneerselvam

Thamaraikkanni Panneerselvam

Solution Architect

lastminute.com group

Udayani V

Udayani V

Senior Member Of Technical Staff

Broadcom

Track: Enterprise Java
Session Type: Workshop

AI in modern applications must go beyond simple prompts to deliver real value—it needs to reason over your data, respect context, and integrate with external systems. In this fast-paced 90-minute workshop, you’ll learn how to power Spring Boot applications with Spring AI. We’ll start with chat completions using ChatClient, then ground responses with Retrieval-Augmented Generation (RAG). From there, we’ll extend the application with Tools and the Model Context Protocol (MCP)—standardizing how AI models call APIs, query data, and orchestrate real-world workflows. By the end, you’ll walk away with a working Spring AI project and a clear roadmap for building production-ready AI agents..

Prerequisites

  1. Make sure the following are installed on your system:
  • Java 21 or higher
  • Maven: For building the project
  • Git: For cloning the repository
  • Docker: We’ll use Docker to run the GitHub MCP Server. You can find installation instructions at the official Docker website
  • Node.js and npx: We’ll use npx to run the MCP Model Inspector. You can install it from the official Node.js website
  • An IDE: Your favorite IDE (Spring Tool Suite, IntelliJ IDEA, VS Code with Spring extensions)
  • An AI Model Provider Account: You’ll need an API key from a provider like OpenAI or Anthropic
  1. Configure Your AI Model Provider

  • Spring AI supports a wide range of LLM providers. For this workshop, you can use any provider, but we have included instructions for three popular options below. For a complete list of supported providers and their specific configurations, please refer to the official Spring AI documentation.

  • Choose one of the following options to configure your connection to a Large Language Model (LLM). Option A: OpenAI Option B: Anthropic

  1. Configure GitHub Access Token

During the workshop, we will connect our Spring AI application to the GitHub MCP Server to interact with GitHub repositories. This requires a GitHub Personal Access Token (PAT).

Create a Personal Access Token: Follow the official GitHub documentation to create a “classic” token.

  1. Verify npx is installed: Run the command: npx –version

  2. Installing PostgreSQL with pgvector Extension The pgvector extension adds vector data type support to PostgreSQL — required for storing and searching embeddings in AI applications. Below are instructions to install PostgreSQL with pgvector enabled on Windows, macOS, and Docker.

Verification

  • To test that vectors work, run in psql:
  • CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3));
  • INSERT INTO items (embedding) VALUES (’[1,2,3]’);
  • SELECT * FROM items;

If it inserts and selects successfully — your Postgres is now vector-enabled 🎉

More details refer: https://docs.google.com/document/d/1t4NLKcxI8DiuKzEOuMVZN6t8Qbz0ML595xrFBr8naYQ/edit?tab=t.0