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Developing a Spring AI Enhanced Restaurant Booking System Employ…

@pacphi

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開発者ツール

ライセンス

Apache-2.0 license

トランスポート

stdio

作者

pacphi

Developing a Spring AI Enhanced Restaurant Booking System Employing an API-first Approach MCP Server について

This multi-module project hosts a client code-generated from an OpenAPI derivative of the ResOs API combined with a Spring AI implementation. It also includes an MCP server, MCP client configuration for use with Claude and a standalone ReactJS powered chatbot UI.

#java#openapi#spring-ai#model-context-protocol

Developing a Spring AI Enhanced Restaurant Booking System Employing an API-first Approach MCP Server の設定

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このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

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Developing a Spring AI Enhanced Restaurant Booking System Employing an API-first Approach MCP Server のツール

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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 Developing a Spring AI Enhanced Restaurant Booking System Employing an API-first Approach?

This MCP server provides tools for searching restaurants and making reservations through an AI-powered chatbot, integrating with the ResOS API or a compatible local backend. It is built with Spring Boot and Spring AI, and supports both Claude Desktop and a standalone ReactJS UI.

How to use Developing a Spring AI Enhanced Restaurant Booking System Employing an API-first Approach?

Configure the server by setting the RESOS_API_ENDPOINT environment variable (defaults to https://api.resos.com/v1). Launch the backend if needed, then run the MCP server jar with Java. For Claude Desktop, add a tool entry in claude_desktop_config.json pointing to the jar. Alternatively, run the chatbot module with an LLM provider API key.

Key features of Developing a Spring AI Enhanced Restaurant Booking System Employing an API-first Approach

  • MCP server for restaurant search and booking tools
  • Integrates with ResOS API or local backend
  • Works with Claude Desktop as MCP client
  • Includes a standalone ReactJS chatbot UI
  • Supports multiple LLM providers (OpenAI, Groq Cloud, OpenRouter)
  • Built with Spring Boot, Spring AI, and Spring Data JDBC

Use cases of Developing a Spring AI Enhanced Restaurant Booking System Employing an API-first Approach

  • Allow users to search for restaurants via natural language chat
  • Let a chatbot make reservations on behalf of a user
  • Integrate restaurant booking capabilities into existing AI assistants
  • Serve as a reference implementation for Spring AI MCP server patterns

よくある質問

1

What dependencies are required to run the MCP server?

Java SDK 21 or better, Maven 3.9.9 or better, and (optionally) a ResOS API key if connecting to the real API. For the chatbot, an LLM provider API key is needed.

2

How do I configure the server to use a local backend?

Set the environment variable `RESOS_API_ENDPOINT=http://localhost:8080/api/v1/resos` and run the backend module first with `mvn clean spring-boot:run -Dspring-boot.run.profiles=dev`.

3

Where does reservation data live?

Data is managed by the backend module, which uses Spring Boot Starter Data JDBC with an embedded database (e.g., H2) when run locally. If using the real ResOS API, data lives in ResOS's cloud service.

4

What transport does the MCP server use?

The MCP server communicates via standard input/output (stdio) when launched as a Java process, as configured in the Claude Desktop JSON.

5

Does the server require authentication?

For the local backend, no API key is needed. When using the real ResOS API, a valid API key is required if

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