what is MianshiyaServer?
MianshiyaServer is an API that allows users to search for interview questions compatible with the MCP protocol, making it the first of its kind in China. It is designed to work with various intelligent assistants that support the MCP protocol.
how to use MianshiyaServer?
To use MianshiyaServer, you need to integrate it with the MCP Java SDK. After cloning the repository and building the project, you can configure it in your application settings to start querying interview questions.
key features of MianshiyaServer?
- Compatibility with the MCP protocol for seamless integration.
- Ability to search for interview questions and retrieve links to them.
- Easy setup and configuration through Java SDK.
use cases of MianshiyaServer?
- Assisting users in preparing for job interviews by providing relevant questions.
- Integrating with AI assistants to enhance their capabilities in interview preparation.
- Offering a centralized platform for accessing a variety of interview questions.
FAQ from MianshiyaServer?
- What is the MCP protocol?
The MCP protocol is a standard for communication between AI models and applications, allowing for easier integration and functionality.
- Is MianshiyaServer free to use?
Yes! MianshiyaServer is free to use for everyone.
- What programming language is required to use MianshiyaServer?
MianshiyaServer is developed in Java, so a Java runtime environment is required.
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
-
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]"));
}
