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.
@lucasdengcn
基本情報
spring ai example. mcp server, agentic ai.
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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.
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.
@Tool annotation@ToolParamIt contains two main modules: `mcp-server` (MCP server with SSE support) and `proposal-agent` (MCP client for AI proposals), plus a common source folder.
The configuration uses the Ollama AI model with PGVector for vector storage and H2 database for development.
It uses Server-Sent Events (SSE) for real‑time communication, implemented with Spring’s `SseEmitter`.
Methods are annotated with `@Tool` to become tools, optionally using `@ToolParam` for parameter descriptions and custom result converters via `resultConverter`.
Yes, the `mcp-server` module implements the MCP server, while the `proposal-agent` module implements the MCP client.
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