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Spring Boot AI MCP Server

@rogervinas

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基本情報

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トランスポート

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作者

rogervinas

Spring Boot AI MCP Server について

🍀 🤖 Spring Boot AI - Evaluation Testing

#mcp#rag#pgvector#ollama#mcp-server#mcp-client#spring-boot-ai#spring-boot-ai-mcp

Spring Boot AI MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "spring-boot-ai": {
      "command": "docker",
      "args": [
        "compose",
        "-f",
        "docker-compose-vectordb.yml",
        "up",
        "-d"
      ]
    }
  }
}

Spring Boot AI MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Spring Boot AI?

Spring Boot AI is a sample application demonstrating how to build an AI agent using Spring AI, the Model Context Protocol (MCP), and Retrieval Augmented Generation (RAG). It shows how to create tools (Weather, Booking), integrate with AI models (Ollama, Gemini, Bedrock), and add chat memory and vector store context.

How to use Spring Boot AI?

Configure the application for your chosen AI provider (Ollama, Gemini, or Bedrock) via Spring profiles. Run the vector database (PGVector) and start the MCP server and chat server. Send requests to the REST endpoint POST /{chatId}/chat?question=... to interact with the agent.

Key features of Spring Boot AI

  • Local and remote MCP tools for weather and booking
  • RAG with PGVector vector store
  • Chat memory via MessageChatMemoryAdvisor
  • Support for Ollama, Gemini, and AWS Bedrock
  • AI Model Evaluation for testing

Use cases of Spring Boot AI

  • Building an AI-powered travel booking assistant
  • Learning how to integrate MCP tools into a Spring Boot application
  • Experimenting with different AI models and embedding providers

よくある質問

1

What AI models does Spring Boot AI support?

It supports Google Gemini, AWS Bedrock, and local Ollama models, switchable via Spring profiles.

2

How are tools implemented in Spring Boot AI?

Tools are created as Spring Beans annotated with `@Tool` and `@ToolParam`, and registered as `MethodToolCallbackProvider`.

3

Does Spring Boot AI use a vector store?

Yes, it uses PGVector for RAG – the vector store is loaded with sample city data at startup.

4

What transport does the MCP server use?

The remote Booking MCP server uses SSE transport, configured via `spring.ai.mcp.client.sse.connections.booking-tool.url`.

5

Can I test the AI agent?

Yes, the project includes AI Model Evaluation using Spring AI’s testing support.

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