面试鸭 MCP Server

面试鸭 MCP Server

By gulihua10010 GitHub

MCP Server面试鸭搜索题目的服务

mcp interview
Overview

What is MCP Server?

MCP Server is a question search service for interview preparation, compatible with the MCP protocol, making it the first of its kind in China. It allows users to search for interview questions and provides links to relevant resources.

How to use MCP Server?

To use MCP Server, you need to integrate it with a Java environment and configure it using the MCP Java SDK. Follow the installation and configuration steps provided in the documentation to set it up.

Key features of MCP Server?

  • Compatibility with the MCP protocol for seamless integration.
  • Question search functionality that retrieves interview questions from the Mianshiya platform.
  • Easy setup and configuration through Java SDK.

Use cases of MCP Server?

  1. Assisting job seekers in preparing for interviews by providing relevant questions.
  2. Integrating with AI assistants to enhance interview preparation tools.
  3. Supporting developers in building applications that require interview question retrieval.

FAQ from MCP Server?

  • What is the MCP protocol?

The MCP protocol is a standard for communication between AI assistants and services, allowing for efficient data exchange.

  • Is there a cost to use MCP Server?

MCP Server is free to use for developers and users.

  • What programming language is required to use MCP Server?

MCP Server is developed in Java, so a Java runtime environment is required for integration.

Content

面试鸭 MCP Server

简介

面试鸭 的题目搜索API现已兼容MCP协议,是国内首家兼容MCP协议的面试刷题网站。关于MCP协议,详见MCP官方文档

依赖MCP Java SDK开发,任意支持MCP协议的智能体助手(如ClaudeCursor以及千帆AppBuilder等)都可以快速接入。

以下会给更出详细的适配说明。

工具列表

题目搜索 questionSearch

  • 将面试题目检索为面试鸭里的题目链接
  • 输入: 题目
  • 输出: [题目](链接)

快速开始

使用面试鸭MCP Server主要通过Java SDK 的形式

Java 接入

前提需要Java运行时环境

安装

git clone https://github.com/gulihua10010/mcp-mianshiya-server

构建

cd mcp-mianshiya-server
mvn clean package

使用

  1. 打开Cherry Studio设置,点击MCP 服务器cherry1.png

  2. 点击编辑 JSON,将以下配置添加到配置文件中。

{
  "mcpServers": {
    "mianshiyaServer": {
      "command": "java",
      "args": [
        "-Dspring.ai.mcp.server.stdio=true",
        "-Dspring.main.web-application-type=none",
        "-Dlogging.pattern.console=",
        "-jar",
        "/yourPath/mcp-server-0.0.1-SNAPSHOT.jar"
      ],
      "env": {}
    }
  }
}

cherry2.png

  1. 在设置-模型服务里选择一个模型,输入API密钥,选择模型设置,勾选下工具函数调用功能。 cherry3.png
  2. 在输入框下面勾选开启MCP服务。 cherry4.png
  3. 配置完成,然后查询下面试题目 cherry5.png

代码调用

  1. 引入依赖
        <dependency>
            <groupId>com.alibaba.cloud.ai</groupId>
            <artifactId>spring-ai-alibaba-starter</artifactId>
            <version>1.0.0-M6.1</version>
        </dependency>
    <dependency>
      <groupId>org.springframework.ai</groupId>
      <artifactId>spring-ai-mcp-client-spring-boot-starter</artifactId>
      <version>1.0.0-M6</version>
    </dependency>
  1. 配置MCP服务器 需要在application.yml中配置MCP服务器的一些参数:
spring:
  ai:
    mcp:
      client:
        stdio:
          # 指定MCP服务器配置文件
          servers-configuration: classpath:/mcp-servers-config.json
  mandatory-file-encoding: UTF-8

其中mcp-servers-config.json的配置如下:

{
  "mcpServers": {
    "mianshiyaServer": {
      "command": "java",
      "args": [
        "-Dspring.ai.mcp.server.stdio=true",
        "-Dspring.main.web-application-type=none",
        "-Dlogging.pattern.console=",
        "-jar",
        "/Users/gulihua/Documents/mcp-server/target/mcp-server-0.0.1-SNAPSHOT.jar"
      ],
      "env": {}
    }
  }
}

客户端我们使用阿里巴巴的通义千问模型,所以引入spring-ai-alibaba-starter依赖,如果你使用的是其他的模型,也可以使用对应的依赖项,比如openAI引入spring-ai-openai-spring-boot-starter 这个依赖就行了。 配置大模型的密钥等信息:

spring:
  ai:
    dashscope:
      api-key: ${通义千问的key}
      chat:
        options:
          model: qwen-max

通义千问的key可以直接去官网 去申请,模型我们用的是通义千问-Max。 3) 初始化聊天客户端

@Bean
public ChatClient initChatClient(ChatClient.Builder chatClientBuilder,
                                 ToolCallbackProvider mcpTools) {
    return chatClientBuilder
    .defaultTools(mcpTools)
    .build();
}
  1. 接口调用
    @PostMapping(value = "/ai/answer/sse", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public Flux<String> generateStreamAsString(@RequestBody AskRequest request) {

        Flux<String> content = chatClient.prompt()
                .user(request.getContent())
                .stream()
                .content();
        return content
                .concatWith(Flux.just("[complete]"));

    }
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