Do I need Spring Boot to use this skill?

While the skill is optimized for Spring Boot with auto-configuration and starters, the fundamental patterns can be adapted for any Java application using the Spring AI framework.

What is an MCP server in the context of Spring AI?

An MCP server uses the Model Context Protocol to expose local tools, resources, and prompts to AI clients like Claude Code, utilizing Spring AI's declarative framework for implementation.

Can I use this with both Maven and Gradle?

Yes, the skill includes configuration patterns and dependency management examples for both Maven and Gradle build systems.

How does this enhance Claude Code's capabilities?

By implementing these patterns, you allow Claude Code to securely interact with your custom Java logic, databases, and APIs through a standardized protocol, making your local code more 'AI-aware'.

Spring AI MCP Integration

Name: Spring AI MCP Integration
Author: giuseppe-trisciuoglio

bygiuseppe-trisciuoglio

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Desarrollo de API

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

Acerca de

This skill provides a comprehensive set of implementation patterns for building Model Context Protocol (MCP) servers within the Spring ecosystem. By leveraging Spring AI's native abstractions like @Tool and @PromptTemplate, it allows developers to seamlessly expose backend services, databases, and custom logic as standardized AI tools. It is particularly useful for enterprise teams building production-ready AI agents that require secure, observable, and scalable integrations with Java-based microservices and Spring Boot applications.

Características Principales

Standardized prompt management with reusable @PromptTemplate patterns
Advanced function calling patterns with built-in validation and error handling
34 GitHub stars
Declarative tool creation using @Tool and @ToolParam annotations
Native Spring Boot auto-configuration for rapid MCP server deployment
Flexible transport layer support including stdio and Spring-managed channels

Casos de Uso

Exposing internal microservice APIs as executable tools for AI agents
Building secure database query interfaces for AI-driven data analysis
Creating standardized enterprise-wide prompt libraries for consistent AI behavior

Acerca de

Características Principales

Standardized prompt management with reusable @PromptTemplate patterns
Advanced function calling patterns with built-in validation and error handling
34 GitHub stars
Declarative tool creation using @Tool and @ToolParam annotations
Native Spring Boot auto-configuration for rapid MCP server deployment
Flexible transport layer support including stdio and Spring-managed channels

Casos de Uso

Exposing internal microservice APIs as executable tools for AI agents
Building secure database query interfaces for AI-driven data analysis
Creating standardized enterprise-wide prompt libraries for consistent AI behavior