spring-ai-mcp-server-patterns

Implement MCP servers with Spring AI using @Tool and @EnableMcpServer.

322|37|Updated Oct 21, 2025

Search Tags:#java #prompts #tools #mcp-server #spring-ai

Ready to run

npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill spring-ai-mcp-server-patterns

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: spring-ai-mcp-server-patterns
Source: https://github.com/giuseppe-trisciuoglio/developer-kit/tree/main/skills/spring-ai/spring-ai-mcp-server-patterns
Command: npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill spring-ai-mcp-server-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Guides you in building MCP servers with Spring AI to expose tools, resources, and prompts in a scalable, secure manner.

Core Features & Use Cases

Declarative MCP server patterns with tools, resources, and prompts.
Integration with Spring security and monitoring for production readiness.
Clear templates for function calling, prompts, and transports.

Quick Start

Start with a minimal MCP server and gradually add tools, resources, and prompts; configure an HTTP or stdio transport.

Frequently Asked Questions about spring-ai-mcp-server-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build an MCP server with Spring AI to expose tools and prompts?▼

MCP servers with Spring AI use declarative patterns via @Tool, @ToolParam, and @PromptTemplate annotations to expose tools, resources, and prompts. Enable the server with @EnableMcpServer, configure HTTP or stdio transport, and Spring AI handles the MCP protocol binding and function calling integration automatically.

Can I integrate Spring AI MCP servers with enterprise Spring Boot applications?▼

Yes. Spring AI MCP servers are designed for production Spring Boot environments. They integrate with Spring Security for authentication, support monitoring instrumentation, and follow declarative patterns that fit standard enterprise architectures requiring external tool integration and function calling capabilities.

What transport options are available for Spring AI MCP servers?▼

Spring AI MCP servers support both HTTP and stdio transports. Choose HTTP for service-to-service communication over the network, or stdio for local process communication. Configure transport during MCP server bootstrap to match your deployment topology and integration requirements.

How do I define reusable prompts and resources in an MCP server?▼

Use @PromptTemplate annotations to define reusable prompt templates within your MCP server. Combine with @Tool-annotated methods to create resources and tools. Spring AI standardizes transport and discovery patterns, enabling clients to query available prompts, tools, and resources through the MCP protocol.

Do I need custom code to handle function calling in Spring AI MCP servers?▼

No. Spring AI MCP servers handle function calling through declarative @Tool and @ToolParam annotations. The framework manages serialization, parameter binding, and MCP protocol compliance, eliminating boilerplate and reducing the surface area for errors in tool invocation.

What are the limitations of stdio versus HTTP transport for MCP servers?▼

Stdio transport is lightweight and suitable for local process integration but limited to single-machine communication. HTTP transport enables remote service-to-service calls and horizontal scaling but introduces network latency and requires transport security configuration. Choose based on deployment topology and security posture.