面试鸭 MCP Server

by yuyuanweb

Interview Duck MCP Server 提供与 MCP 协议兼容的问题搜索 API，支持与 MCP 兼容的智能助手集成。需要 Java 17 运行环境。服务器以从源代码构建的 Java jar 文件形式运行。配置涉及指定已构建的 jar 文件路径。

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Repository-wide counts · Cached 2026-03-07

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The 面试鸭 MCP Server MCP server is a publicly available project. Review the upstream repository for installation instructions, supported tools, compatibility, permissions, and current maintenance status.

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一组用于模型上下文协议（MCP）的参考实现，展示了对大型语言模型（LLM）工具和数据源的安全且受控的访问方式。

Context7 MCP server providing up-to-date, version-specific documentation and code examples for libraries, enabling coding agents to fetch accurate docs and code snippets. Requires an API key for higher rate limits, passed via CONTEXT7_API_KEY header.

A Model Context Protocol (MCP) server that provides browser automation capabilities using Playwright. Enables LLMs to interact with web pages through structured accessibility snapshots, bypassing the need for screenshots or visually-tuned models.

AIHawk is an anti detect browser and web browsing agent, open source, with an MCP server for coding agents: undetected, no captchas, no blocks. It requires an OpenRouter API key for the standalone web UI mode, which can be provided via the --openrouter-key flag or the OPENROUTER_API_KEY environment variable or a .env file in the running directory.

FROM THE SOURCE

Repository README

Build-time snapshot · Retrieved 2026-10-05

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Claude、Cursor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 17 运行时环境

安装

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

构建

cd mcp-mianshiya-server
mvn clean package

使用

打开Cherry Studio的设置，点击MCP 服务器。
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": {}
    }
  }
}

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。
在输入框下面勾选开启MCP服务。
配置完成，然后查询下面试题目

代码调用

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>

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();
}

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]"));

    }