Spring AI Example

Name: Spring AI Example
Author: lucasdengcn

@lucasdengcn

代码仓库

关于 Spring AI Example

spring ai example. mcp server, agentic ai.

#spring #springai #mcp-server

基本信息

分类

其他

运行时

java

传输方式

stdio

发布者

lucasdengcn

配置

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代码仓库

工具

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概览

What is Spring AI Example?

This project demonstrates implementation patterns and best practices for using Spring AI tools, integrating with the Model Context Protocol (MCP) through a server module (with WebFlux and WebMvc SSE support) and a client module for building AI-powered proposals. It is designed for developers working with Spring AI, MCP, and large language models.

How to use Spring AI Example?

Clone the repository and run the Spring Boot application. Configure the Ollama AI model, PGVector vector store, and H2 database in the application properties. The mcp-server module can be invoked via SSE endpoints, while the proposal-agent module acts as an MCP client.

Key features of Spring AI Example

Methods as tools via @Tool annotation
Custom tool result converters
Tool context injection in methods
Tool parameter descriptions with @ToolParam
MCP server with SSE support
MCP client for AI proposal generation

Use cases of Spring AI Example

Building chatbots that retrieve current date/time or customer information
Creating proposal generation systems with tool-based data access
Demonstrating Spring AI tool patterns in an MCP architecture
Testing real-time SSE updates from MCP servers

FAQ from Spring AI Example

What is the project structure?

It contains two main modules: mcp-server (MCP server with SSE support) and proposal-agent (MCP client for AI proposals), plus a common source folder.

What AI model and database are used?

The configuration uses the Ollama AI model with PGVector for vector storage and H2 database for development.

What transport does the MCP server use?

It uses Server-Sent Events (SSE) for real‑time communication, implemented with Spring’s SseEmitter.

How are tools defined in Spring AI?

Methods are annotated with @Tool to become tools, optionally using @ToolParam for parameter descriptions and custom result converters via resultConverter.