Install
$ agentstack add mcp-yuyuanweb-mcp-mianshiya-server ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README — it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming — see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps — measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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
``` bash git clone https://github.com/yuyuanweb/mcp-mianshiya-server
#### 构建
``` bash
cd mcp-mianshiya-server
mvn clean package
使用
1) 打开Cherry Studio的设置,点击MCP 服务器。
2) 点击编辑 JSON,将以下配置添加到配置文件中。
``` json { "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": {} } } }
3) 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。
4) 在输入框下面勾选开启MCP服务。
5) 配置完成,然后查询下面试题目
#### 代码调用
1) 引入依赖
``` java
com.alibaba.cloud.ai
spring-ai-alibaba-starter
1.0.0-M6.1
org.springframework.ai
spring-ai-mcp-client-spring-boot-starter
1.0.0-M6
2) 配置MCP服务器 需要在application.yml中配置MCP服务器的一些参数:
``` yaml 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:
``` json
{
"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:
``` yaml 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](https://help.aliyun.com/zh/model-studio/developer-reference/get-api-key?spm=a2c4g.11186623.0.0.7399482394LUBH) 去申请,模型我们用的是通义千问-Max。
3) 初始化聊天客户端
``` java
@Bean
public ChatClient initChatClient(ChatClient.Builder chatClientBuilder,
ToolCallbackProvider mcpTools) {
return chatClientBuilder
.defaultTools(mcpTools)
.build();
}
4) 接口调用
``` java @PostMapping(value = "/ai/answer/sse", produces = MediaType.TEXTEVENTSTREAM_VALUE) public Flux generateStreamAsString(@RequestBody AskRequest request) {
Flux content = chatClient.prompt() .user(request.getContent()) .stream() .content(); return content .concatWith(Flux.just("[complete]"));
}
## Source & license
This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.
- **Author:** [yuyuanweb](https://github.com/yuyuanweb)
- **Source:** [yuyuanweb/mcp-mianshiya-server](https://github.com/yuyuanweb/mcp-mianshiya-server)
- **License:** MIT
- **Homepage:** https://www.mianshiya.com
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.