mcp-mianshiya-server

# Interview Duck MCP Server ## Introduction The question search API of [Interview Duck](https://mianshiya.com/) is now compatible with the MCP protocol, making it the first interview question website in China to support the MCP protocol. For more information about the MCP protocol, please refer to the official [documentation](https://modelcontextprotocol.io/). Developed using the `MCP Java SDK`, any intelligent assistant that supports the MCP protocol (such as `Claude`, `Cursor`, and `Qianfan AppBuilder`, etc.) can be quickly integrated. Detailed adaptation instructions will be provided below. ## Tool List #### Question Search `questionSearch` - Retrieves interview questions as links from Interview Duck - Input: `Question` - Output: `[Question](Link)` ## Quick Start Using the Interview Duck MCP Server is primarily done through the `Java SDK`. ### Java Integration > Prerequisite: Java 17 runtime environment #### Installation ```bash git clone https://github.com/yuyuanweb/mcp-mianshiya-server ``` #### Build ```bash cd mcp-mianshiya-server mvn clean package ``` #### Usage 1) Open `Cherry Studio` settings and click on `MCP Server`.  2) Click on `Edit JSON` and add the following configuration to the configuration file. ```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) In the settings under model services, select a model, input the API key, choose model settings, and check the tool function call feature.  4) Check the box to enable the MCP service below the input box.  5) Configuration is complete, and you can now query interview questions.  #### Code Invocation 1) Add dependencies ```java <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> ``` 2) Configure the MCP server You need to configure some parameters of the MCP server in the application.yml: ```yaml spring: ai: mcp: client: stdio: # Specify the MCP server configuration file servers-configuration: classpath:/mcp-servers-config.json mandatory-file-encoding: UTF-8 ``` The configuration for mcp-servers-config.json is as follows: ```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": {} } } } ``` For the client, we are using Alibaba's Tongyi Qianwen model, so we include the spring-ai-alibaba-starter dependency. If you are using another model, you can use the corresponding dependency, such as for OpenAI, just include the `spring-ai-openai-spring-boot-starter` dependency. Configure the key and other information for the large model: ```yaml spring: ai: dashscope: api-key: ${Tongyi Qianwen key} chat: options: model: qwen-max ``` You can apply for the Tongyi Qianwen key directly from the [official website](https://help.aliyun.com/zh/model-studio/developer-reference/get-api-key?spm=a2c4g.11186623.0.0.7399482394LUBH). The model we are using is Tongyi Qianwen-Max. 3) Initialize the chat client ```java @Bean public ChatClient initChatClient(ChatClient.Builder chatClientBuilder, ToolCallbackProvider mcpTools) { return chatClientBuilder .defaultTools(mcpTools) .build(); } ``` 4) API call ```java @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]")); } ```