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Spring AI + MCP: Build Distributed AI Systems with Java

Name: Spring AI + MCP: Build Distributed AI Systems with Java
Rating: 4.7 (57 reviews)

Build AI assistants using Spring AI, Model Context Protocol (MCP), and microservices with real enterprise architecture.

Highest Rated

Created byInfiproton Tech, Harish B N

Last updated 4/2026

English

What you'll learn

Build a distributed AI system using Spring AI and Model Context Protocol (MCP) across multiple Spring Boot microservices.
Convert Spring Boot microservices into MCP tool providers using Spring AI MCP Server.
Implement an MCP client AI assistant that dynamically discovers and executes tools across services.
Understand why hardcoded AI orchestration fails and how capability-driven MCP architectures solve it.
Analyze and debug MCP systems using JSON-RPC logs, tool schemas, and runtime tool execution.
Implement advanced MCP capabilities including prompts, resources, and MCP transport modes.

Course content

8 sections • 44 lectures • 3h 16m total length

The Finished MCP System in Action2:46
Demo of the full NexaCorp MCP system running across microservices
High-Level Architecture Deep Dive3:06
Explore the distributed architecture and MCP client/server roles.
Why Hardcoded AI Orchestration Fails2:34
Understand the limitations of naive AI workflow orchestration.
Course Roadmap & Learning Path2:46
Overview of how the system will be built throughout the course.
Free IntelliJ IDEA (90 Days)0:41
Get IntelliJ IDEA Ultimate for free and follow this course using a professional development environment.
Redeem your 90-day access and get started quickly.
Accessing Source Code (GitHub Guide)0:58

NexaCorp Domain & Service Architecture2:43
Understand the business domains and microservice boundaries.
Building hr-service with PostgreSQL and Flyway10:52
Create the HR service with schema isolation and migrations.
Building deployment-service Microservice9:32
Implement deployment domain APIs using the same architecture pattern.
Adding notification-service and ticket-service4:46
Integrate supporting services and verify their endpoints
End-to-End Backend Verification4:14
Test all microservices and confirm database schema isolation.

Why Add AI to the Backend System?4:36
Understand how AI integrates into enterprise workflows.
Creating the ai-assistant-naive Service7:33
Build the first assistant service with Spring AI and OpenAI.
Intent Extraction Using LLM5:36
Extract structured intent from natural language prompts.
Implementing Manual Orchestration Logic10:46
Coordinate HR and Deployment services using REST calls.
Identifying Architectural Limitations5:15
Analyze the scalability problems of hardcoded orchestration.

From Hardcoded Orchestration to Capability Exposure5:03
Shift from workflow-driven design to capability-based architecture.
What Is Model Context Protocol (MCP)?5:39
Understand how MCP standardizes AI tool interactions.
Understanding JSON-RPC in MCP4:14
Learn how MCP uses JSON-RPC for communication.
MCP Capabilities Overview5:07
Explore tools, prompts, resources, and completions capabilities.
Transport Modes in MCP4:04
Understand stateless and streaming MCP transports.
Live MCP Teaser Using a POC Project4:22
Preview real MCP tool interactions before implementation.

Preparing hr-service for MCP3:21
Enable MCP server support in the HR microservice.
Exposing the First MCP Tool5:22
Create the first MCP tool using the @McpTool annotation.
Exposing Additional HR Capabilities3:52
Add multiple MCP tools and inspect generated schemas.
Converting deployment-service to MCP4:15
Expose deployment management capabilities as MCP tools.
Verifying Multiple MCP Tool Providers2:36
Validate multiple services acting as independent MCP servers.

Designing the MCP-Based Assistant3:35
Understand the architecture of an MCP client assistant.
Creating the ai-assistant-mcp Service4:58
Build the assistant service with Spring AI and OpenAI.
Enabling MCP Tool Integration5:13
Connect MCP servers and expose tools to the LLM.
Handling Timestamp Parsing for MCP Tool Arguments0:52
First End-to-End MCP Workflow3:15
Observe dynamic multi-service orchestration using MCP.
How the LLM Orchestrates MCP Tools3:17
Analyze logs to understand LLM planning and execution.

Understanding MCP Logs5:23
Learn to interpret MCP discovery and execution logs.
Partial Workflow Execution2:47
Observe how LLMs dynamically select required tools.
Handling Invalid Tool Inputs3:14
Understand tool validation and LLM response behavior.
Extending the System with a New MCP Tool Provider6:58
Add a new MCP server and observe automatic tool discovery.
Inspecting MCP Tool Schemas4:45
Analyze tool schemas and how LLMs construct tool calls.

When Tools Are Not Enough2:19
Understand why MCP provides capabilities beyond tools.
Implementing MCP Prompts (Server Side)5:50
Expose reusable prompts from ticket-service.
Using MCP Prompts in the Assistant6:34
Retrieve and execute MCP prompts within the assistant.
Understanding MCP Resources and Their Limitations3:23
Learn when resources are appropriate and their constraints.
Understanding MCP Completions3:53
Explore the role of completions in developer tooling.
Stateless vs Streamable MCP Transport3:43
Understand streaming MCP sessions and when to use them.

Requirements

Basic Java programming knowledge (classes, REST APIs, Maven/Gradle).
Familiarity with Spring Boot fundamentals (controllers, services, configuration).
Basic understanding of REST APIs and microservice architecture.
A development environment such as IntelliJ IDEA or VS Code.
An OpenAI API key to run the AI assistant examples.

Description

Modern AI systems are no longer simple chatbots.
Real-world applications require AI assistants that can interact with backend services, execute actions, retrieve data, and coordinate workflows across distributed systems.

In this course, you will learn how to build these systems using Spring AI and Model Context Protocol (MCP).

Instead of toy examples, you will implement a complete distributed AI architecture built with Spring Boot microservices. The course is based on a realistic enterprise system called NexaCorp, where an AI assistant interacts with services such as HR, deployment management, notifications, and ticket management.

Includes free 90-day access to IntelliJ IDEA Ultimate for a professional development experience.

What you will build

During this course you will build a production-style AI system that includes:

Multiple Spring Boot microservices
A PostgreSQL database with schema-per-service isolation
A naive AI assistant with manual orchestration
An MCP-based AI assistant with dynamic tool discovery
Distributed AI workflows across multiple services

You will see how an AI assistant can coordinate operations like:

Applying employee leave
Finding a replacement engineer
Reassigning deployments
Triggering notifications across services

Course implementation highlights

This course is fully hands-on and covers:

Enterprise backend setup

Build multiple Spring Boot microservices
Use PostgreSQL with schema-per-service architecture
Manage schema and seed data using Flyway
Verify service isolation and inter-service communication

Naive AI orchestration

Build an AI assistant using Spring AI
Extract structured intent from natural language
Implement manual orchestration using REST APIs
Understand the limitations of hardcoded AI workflows

Model Context Protocol (MCP)

Understand MCP architecture and JSON-RPC communication
Convert microservices into MCP tool providers
Expose domain capabilities using Spring AI MCP server
Inspect tool schemas generated automatically

MCP-based AI assistant

Build an MCP client assistant using Spring AI
Enable dynamic tool discovery across services
Allow the LLM to plan and execute workflows
Remove orchestration logic from application code

Debugging and runtime analysis

Inspect MCP logs and tool execution flows
Understand JSON-RPC tool interactions
Handle tool errors and partial workflow execution
Extend the system with new MCP tool providers

Advanced MCP capabilities

The course also explores additional MCP features including:

Prompts capability for reusable reasoning instructions
Resources capability for structured artifacts
Completions capability and when it is used
Stateless vs streaming MCP transport models

Technologies used

Java
Spring Boot
Spring AI
Model Context Protocol (MCP)
PostgreSQL
Flyway
Gradle
Docker

Who this course is for:

Java developers who want to integrate AI capabilities into Spring Boot microservices.
Backend engineers interested in building AI-powered systems using Spring AI and MCP.
Software architects exploring distributed AI orchestration and capability-driven architectures.
Developers building AI assistants or AI agents that interact with real backend services.
Engineers curious about Model Context Protocol (MCP) and how to implement it in production-style systems.

Spring AI + MCP: Build Distributed AI Systems with Java

What you'll learn

Explore related topics

Course content

Live MCP System Demo & Course Orientation6 lectures • 13min

Building the Enterprise Domain Microservices5 lectures • 32min

Building the Naive AI Assistant5 lectures • 34min

Introduction to Model Context Protocol (MCP)6 lectures • 28min

Converting Microservices into MCP Servers5 lectures • 19min

Building the MCP Client Assistant6 lectures • 21min

Proving Distributed Intelligence5 lectures • 23min

Beyond Tools: Advanced MCP Capabilities6 lectures • 26min

Requirements

Description

Who this course is for: