📄️ Module 1: Why Spring AI + MCP?
Understand what Spring AI adds to the MCP stack, why the combination is more than the sum of its parts, and what you'll build in this course.
📄️ Module 2: Environment Setup
Create a Spring Boot project with Spring AI and MCP dependencies. Configure API keys, understand the BOM, and verify your setup with a smoke test.
📄️ Module 3: Connecting to Your First MCP Server
Configure a stdio MCP connection, inspect the tools it exposes, and understand the connection lifecycle. Build a tool-lister that shows what's available.
📄️ Module 4: ChatClient with MCP Tools
Build a conversational AI agent that uses MCP tools automatically. Understand the tool-use loop, conversation memory, system prompts, and streaming responses.
📄️ Module 5: Multi-Server Agents
Connect to multiple MCP servers simultaneously. Build an agent with specialised capabilities from different servers and understand tool routing.
📄️ Module 6: Exposing Spring Beans as MCP Tools
Turn your existing Spring services into MCP tools using the @McpTool annotation (Spring AI 2.0) and spring-ai-starter-mcp-server. Connect Claude Desktop directly to your Spring Boot application.
📄️ Module 7: Testing Spring AI + MCP Applications
Test AI-integrated applications reliably. Unit-test tool methods directly, integration-test MCP servers with a test client, and mock the chat model for deterministic tests.
📄️ Module 8: Production Configuration
Ship a Spring AI + MCP application to production. Externalise configuration, add health indicators, instrument with Micrometer metrics, configure retries, and handle failures gracefully.