This version is still in development and is not considered stable yet. For the latest snapshot version, please use Spring AI 1.0.2!

Getting Started with Model Context Protocol (MCP)

The Model Context Protocol (MCP) standardizes how AI applications interact with external tools and resources.

Spring joined the MCP ecosystem early as a key contributor, helping to develop and maintain the official MCP Java SDK that serves as the foundation for Java-based MCP implementations. Building on this contribution, Spring AI provides MCP support through Boot Starters and annotations, making it easy to build both MCP servers and clients.

Introduction Video

Introduction to Model Context Protocol (MCP) - YouTube

Start here for an introductory overview of the Model Context Protocol, explaining core concepts and architecture.

Complete Tutorial and Source Code

đź“– Blog Tutorial: Connect Your AI to Everything

đź’» Complete Source Code: MCP Weather Example Repository

The tutorial covers the essentials of MCP development with Spring AI, including advanced features, and deployment patterns. All code examples below are taken from this tutorial.

Quick Start

The fastest way to get started is with Spring AI’s annotation-based approach. The following examples are from the blog tutorial:

Simple MCP Server

@Service
public class WeatherService {

    @McpTool(description = "Get current temperature for a location")
    public String getTemperature(
            @McpToolParam(description = "City name", required = true) String city) {
        return String.format("Current temperature in %s: 22°C", city);
    }
}

Add the dependency and configure:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-mcp-server-webmvc</artifactId>
</dependency>
spring.ai.mcp.server.protocol=STREAMABLE

Simple MCP Client

@Bean
public CommandLineRunner demo(ChatClient chatClient, ToolCallbackProvider mcpTools) {
    return args -> {
        String response = chatClient
            .prompt("What's the weather like in Paris?")
            .toolCallbacks(mcpTools)
            .call()
            .content();
        System.out.println(response);
    };
}

Add the dependency and configure:

<dependency>
  <groupId>org.springframework.ai</groupId>
  <artifactId>spring-ai-starter-mcp-client</artifactId>
</dependency>
spring:
  ai:
    mcp:
      client:
        streamable-http:
          connections:
            weather-server:
              url: http://localhost:8080

Learning Resources

Implementation Video

Spring AI Model Context Protocol (MCP) Integration - YouTube

A video walkthrough of Spring AI’s MCP integration, covering both server and client implementations.

Additional Examples Repository

Beyond the tutorial examples, the Spring AI Examples repository contains numerous MCP implementations.

Recommended Starting Points

Annotation-based examples

By Use Case

Weather Services:

Data Integration:

Web Integration:

Client Examples:

Community Resources

Reference Documentation