Discover Awesome MCP Servers

AI Makerspace MCP Demo Server

Enables web search capabilities through the Tavily API and serves as a demonstration platform for building custom MCP tools. Designed for educational purposes to showcase MCP server development and LangGraph integration.

Bear MCP Server

一个模型上下文协议服务器，允许像 Claude 这样的人工智能助手以安全的只读模式读取来自 Bear 笔记应用程序的笔记。

MCP SubMatcher

An MCP server that automatically renames local subtitle files to match corresponding videos using statistical token matching and episode verification. It enables users to scan media directories, preview matches, and manage subtitle configurations through natural language commands.

MCP Hardware Access Library

A Python framework that enables secure hardware control through the Model Context Protocol, allowing AI agents and automation systems to interact with physical devices across multiple platforms.

figma-pilot

Enables AI agents to create, modify, and manage Figma designs through natural language commands via a specialized MCP server and plugin bridge. It supports a wide range of operations including element creation, property modification, component management, and accessibility checks.

Excel Chart MCP Server

A dual-mode intelligent Excel processing server that provides Excel data analysis tools for Cursor AI in MCP mode and offers a standalone web interface with support for external AI configurations like DeepSeek.

Secure Command Executor MCP Server

一个强大的命令执行服务，具有每日日志轮换功能，旨在安全地管理和执行系统命令，并具有安全检查和日志记录功能。

Chargebee MCP Server

A server that integrates with AI-powered code editors to provide immediate answers about Chargebee products and API services, offering context-aware code snippets and access to Chargebee's knowledge base.

Nexus MCP for Obsidian

Turns your Obsidian vault into an MCP-enabled workspace with tools for reading/writing notes, managing folders, running semantic searches, and maintaining long-term memory—all while keeping data local to your vault.

Azure AI Agent Service MCP Server

Enables connections to Azure AI Agents within any MCP client, allowing users to leverage Azure AI Foundry's models and knowledge tools like Azure AI Search and Bing Web Grounding through a conversational interface.

hello-mcp-server-current-time

好的，这是一个基于 `spring-ai-starter-mcp-server` 的自定义 MCP Server 简单示例，用于获取当前时间。 我将提供代码示例，并解释关键部分。 **1. 项目结构 (假设 Maven 项目)** ``` my-mcp-server/ ├── pom.xml └── src/ └── main/ ├── java/ │ └── com/example/ │ └── mcp/ │ ├── config/ │ │ └── McpServerConfig.java // MCP Server 配置 │ └── controller/ │ └── TimeController.java // 处理时间请求的 Controller └── resources/ └── application.properties // 配置文件 ``` **2. `pom.xml` (Maven 依赖)** ```xml <?xml version="1.0" encoding="UTF-8"?> <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <parent> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-parent</artifactId> <version>3.2.0</version> <!-- 使用最新的 Spring Boot 版本 --> <relativePath/> <!-- lookup parent from repository --> </parent> <groupId>com.example</groupId> <artifactId>my-mcp-server</artifactId> <version>0.0.1-SNAPSHOT</version> <name>my-mcp-server</name> <description>Demo project for Spring Boot MCP Server</description> <properties> <java.version>17</java.version> <spring-ai.version>1.0.0-M2</spring-ai.version> </properties> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <dependency> <groupId>org.springframework.ai</groupId> <artifactId>spring-ai-core</artifactId> <version>${spring-ai.version}</version> </dependency> <dependency> <groupId>org.springframework.ai</groupId> <artifactId>spring-ai-spring-boot-starter-mcp-server</artifactId> <version>${spring-ai.version}</version> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> <scope>test</scope> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> </plugin> </plugins> </build> </project> ``` **关键依赖:** * `spring-boot-starter-web`: 提供 Spring Web MVC 功能，用于创建 RESTful API。 * `spring-ai-core`: Spring AI 核心库。 * `spring-ai-spring-boot-starter-mcp-server`: Spring AI MCP Server 启动器。 **3. `application.properties` (配置文件)** ```properties # 端口号 server.port=8080 # MCP Server 配置 (可选，使用默认值即可) spring.ai.mcp.server.enabled=true spring.ai.mcp.server.path=/mcp ``` **4. `McpServerConfig.java` (MCP Server 配置)** ```java package com.example.mcp.config; import org.springframework.context.annotation.Configuration; import org.springframework.context.annotation.Bean; import org.springframework.ai.autoconfigure.AiServiceProperties; import org.springframework.ai.autoconfigure.McpServerProperties; import org.springframework.ai.mcp.server.AiServiceHandlerFunction; import org.springframework.ai.mcp.server.McpServerEndpoint; import org.springframework.ai.mcp.server.McpServerFunctionRegistry; import org.springframework.ai.mcp.server.support.DefaultMcpServerFunctionRegistry; import org.springframework.boot.autoconfigure.condition.ConditionalOnMissingBean; import org.springframework.context.ApplicationContext; import org.springframework.web.servlet.function.RouterFunction; import org.springframework.web.servlet.function.ServerResponse; import java.util.List; import static org.springframework.web.servlet.function.RouterFunctions.route; import static org.springframework.web.servlet.function.RequestPredicates.POST; @Configuration public class McpServerConfig { @Bean @ConditionalOnMissingBean public McpServerFunctionRegistry mcpServerFunctionRegistry() { return new DefaultMcpServerFunctionRegistry(); } @Bean public McpServerEndpoint mcpServerEndpoint(McpServerProperties mcpServerProperties, McpServerFunctionRegistry mcpServerFunctionRegistry, ApplicationContext applicationContext, List<AiServiceHandlerFunction> aiServiceHandlerFunctions) { return new McpServerEndpoint(mcpServerProperties, mcpServerFunctionRegistry, applicationContext, aiServiceHandlerFunctions); } @Bean public RouterFunction<ServerResponse> timeRouterFunction(TimeController timeController, McpServerProperties mcpServerProperties) { return route(POST(mcpServerProperties.getPath() + "/time"), timeController::getTime); } } ``` **5. `TimeController.java` (处理时间请求的 Controller)** ```java package com.example.mcp.controller; import org.springframework.stereotype.Component; import org.springframework.web.servlet.function.ServerRequest; import org.springframework.web.servlet.function.ServerResponse; import java.time.LocalDateTime; import java.time.format.DateTimeFormatter; import static org.springframework.web.servlet.function.ServerResponse.ok; @Component public class TimeController { public ServerResponse getTime(ServerRequest request) { LocalDateTime now = LocalDateTime.now(); DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss"); String formattedDateTime = now.format(formatter); return ok().body(formattedDateTime); } } ``` **解释:** * `@Component`: 将 `TimeController` 标记为一个 Spring 组件，使其可以被自动注入。 * `getTime(ServerRequest request)`: 处理 `/mcp/time` POST 请求的方法。 * `LocalDateTime.now()`: 获取当前时间。 * `DateTimeFormatter`: 格式化时间为 `yyyy-MM-dd HH:mm:ss` 格式。 * `ok().body(formattedDateTime)`: 返回 HTTP 200 OK 状态码，并将格式化后的时间作为响应体。 **6. 启动应用程序** 运行 Spring Boot 应用程序。 **7. 测试** 使用 `curl` 或 Postman 等工具发送 POST 请求到 `/mcp/time`： ```bash curl -X POST http://localhost:8080/mcp/time ``` **预期响应:** ``` 2023-11-21 10:30:00 (实际时间会不同) ``` **总结:** 这个示例展示了如何使用 `spring-ai-starter-mcp-server` 创建一个简单的 MCP Server，并添加自定义的端点来处理特定的请求。 关键步骤包括： 1. 添加必要的 Maven 依赖。 2. 配置 `application.properties`。 3. 创建一个 Controller 来处理请求。 4. 创建一个配置类，将Controller注册到RouterFunction中。 5. 启动应用程序并测试端点。 **重要提示:** * 确保你已经安装了 Java 17 或更高版本。 * 根据你的实际需求调整时间格式。 * 这个示例非常简单，你可以根据需要添加更复杂的功能，例如身份验证、授权、数据验证等。 * 请根据实际情况调整 Spring AI 的版本号。 * 如果遇到问题，请检查日志文件以获取更多信息。 这个例子应该能帮助你开始构建自己的自定义 MCP Server。 如果你有任何问题，请随时提出。

Maven Dependencies Server

一个 MCP (模型上下文协议) 服务器，提供用于检查 Maven 依赖项版本的工具。该服务器使 LLM 能够验证 Maven 依赖项并从 Maven 中央仓库检索其最新版本。

Dangerous MCP

一个演示服务器，通过访问敏感环境变量来揭示安全风险，说明 MCP 工具如何在未经明确同意的情况下潜在地泄露用户数据。

Remote MCP Server Authless

A serverless solution for deploying Model Context Protocol (MCP) servers on Cloudflare Workers without authentication requirements, enabling developers to create and access custom AI tools through the MCP standard.

Consumer Rights Wiki MCP Server

Enables AI assistants to access the Consumer Rights Wiki, providing tools to search and retrieve information about modern consumer exploitation issues like privacy violations, dark patterns, and deceptive pricing practices.

Beagle Security MCP Server

Enables integration with Beagle Security API for managing security testing projects, applications, domain verification, and automated penetration tests. Provides 18 tools for creating, monitoring, and retrieving results from security assessments.

mtw-e2e-runner

JSON-driven E2E test runner for AI agents. Define browser tests as JSON action arrays and run them in parallel against a Chrome pool (browserless/chrome) with 28+ built-in actions, visual verification, network debugging, and flaky test detection.

Tavily Search

I am sorry, I cannot directly use Tavily Search to search for news pages or images. I am a language model and do not have the capability to access external search engines or browse the internet.

Cloud SQL Admin MCP Server

An MCP Server that enables interaction with Google Cloud SQL Admin API, allowing users to manage Cloud SQL database instances through natural language commands.

Facebook Ads MCP Server

使用 Claude AI 的 MCP 服务器，用于在 Facebook 上输入和评估广告活动。

Uber MCP Server

Enables AI assistants to interact with the Uber API for ride management, including requesting rides, obtaining price and time estimates, and tracking active trip status. It supports comprehensive journey features such as viewing ride history, cancelling requests, and rating drivers through a secure OAuth 2.0 integration.

My Awesome MCP

A basic MCP server template built with FastMCP framework that provides example tools for echoing messages and retrieving server information. Serves as a starting point for building custom MCP servers with both stdio and HTTP transport support.

even-better-playwright-mcp

An advanced Playwright MCP server that enables efficient browser automation through compressed accessibility snapshots, sandboxed code execution, and visual element labeling. It features 90% DOM compression and integrated DevTools for deep web inspection and interaction.

RednoteMCP

Enables automated interaction with Xiaohongshu (Little Red Book) to search for notes, retrieve content or comments, and post targeted smart comments. It utilizes Playwright to manage browser-based tasks such as secure login and automated engagement.

Google Workspace CRM MCP Server

A Python MCP server that provides a lightweight CRM by integrating Google Sheets, Gmail, and Google Docs for contact management, email communication, and document creation. It features 30 specialized tools, OAuth2 endpoint protection, and a comprehensive audit logging system.

Placeholder Image Generator

Generates customizable placeholder images with configurable dimensions, colors, and text using HTML5 Canvas. Supports multiple formats (PNG/JPEG) with automatic text scaling and contrast detection.

Python MCP Sandbox

一个交互式的 Python 代码执行环境，允许用户和大型语言模型 (LLM) 在隔离的 Docker 容器中安全地执行 Python 代码并安装软件包。

MCP File Compaction

Reduces Claude's context window costs by automatically summarizing inactive files to their public interfaces using AST parsing, keeping only the full contents of the currently active file.

Remote MCP Server (Authless)

Enables deployment of MCP servers on Cloudflare Workers without authentication requirements. Provides a template for creating custom MCP tools that can be accessed remotely via Cloudflare AI Playground or local MCP clients like Claude Desktop.