What is MCP in the context of Spring AI?

MCP stands for Model Context Protocol, a standard that allows AI models to interact with external tools and data; Spring AI provides the native abstractions to build these servers in Java.

What is the benefit of using @Tool annotations?

The @Tool annotation allows you to transform regular Java methods into executable functions that an AI can discover and call, complete with metadata and parameter descriptions.

Can I use this with existing Spring Boot applications?

Yes, the skill includes patterns for Spring Boot auto-configuration, making it easy to integrate MCP server capabilities into your existing microservices.

Does this require a specific LLM provider?

No, while the examples might show OpenAI, Spring AI's MCP implementation is designed to be provider-agnostic, working with any model that supports function calling.

Spring AI MCP Server Patterns

Name: Spring AI MCP Server Patterns
Author: giuseppe-trisciuoglio

bygiuseppe-trisciuoglio

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API開発

Implements Model Context Protocol (MCP) servers using Spring AI to extend AI capabilities with enterprise-grade tools, resources, and prompt templates.

概要

This skill provides a comprehensive set of implementation patterns for building Model Context Protocol (MCP) servers within the Spring AI ecosystem. It enables developers to expose Java methods as AI-callable tools, manage reusable prompt templates, and integrate external data sources via standardized MCP abstractions. By leveraging Spring Boot's auto-configuration and declarative programming model, it streamlines the creation of production-ready AI agents and microservices that can seamlessly communicate with LLMs through a unified protocol.

主な機能

44 GitHub stars
Native integration with Spring Security, monitoring, and existing services
Standardized MCP server implementation with Spring Boot auto-configuration
Support for multiple transport protocols including stdio and web-based channels
Declarative tool creation using @Tool and @ToolParam annotations
Reusable prompt template management for consistent AI interactions

ユースケース

Exposing legacy Java business logic as executable tools for Claude Code via the MCP protocol
Standardizing AI prompt patterns across microservices using shared templates
Building enterprise-grade AI agents that need secure access to internal databases and APIs

概要

主な機能

44 GitHub stars
Native integration with Spring Security, monitoring, and existing services
Standardized MCP server implementation with Spring Boot auto-configuration
Support for multiple transport protocols including stdio and web-based channels
Declarative tool creation using @Tool and @ToolParam annotations
Reusable prompt template management for consistent AI interactions

ユースケース

Exposing legacy Java business logic as executable tools for Claude Code via the MCP protocol
Standardizing AI prompt patterns across microservices using shared templates
Building enterprise-grade AI agents that need secure access to internal databases and APIs