Discover Awesome MCP Servers

mcp-servers

Máy chủ MCP trên nền tảng serverless

Reddit MCP Server

An MCP server that enables AI assistants to access and interact with Reddit content through features like user analysis, post retrieval, subreddit statistics, and authenticated posting capabilities.

Washington Law MCP Server

Provides offline access to Washington State's Revised Code of Washington (RCW) and Washington Administrative Code (WAC) for AI agents. Enables fast retrieval, full-text search, and navigation of all Washington state laws through natural language queries.

SAP BusinessObjects BI MCP Server by CData

This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out our free CData MCP Server for SAP BusinessObjects BI (beta): https://www.cdata.com/download/download.aspx?sku=GJZK-V&type=beta

Mcp Akshare

AKShare là một thư viện giao diện dữ liệu tài chính dựa trên Python, với mục đích hiện thực hóa một bộ công cụ từ thu thập dữ liệu, làm sạch dữ liệu đến lưu trữ dữ liệu cho dữ liệu cơ bản, dữ liệu giá thời gian thực và lịch sử, dữ liệu phái sinh của các sản phẩm tài chính như cổ phiếu, hợp đồng tương lai, quyền chọn, quỹ, ngoại hối, trái phiếu, chỉ số, tiền điện tử, chủ yếu được sử dụng cho mục đích nghiên cứu học thuật.

ETH Price Current Server

A minimal Model Context Protocol (MCP) server that fetches the current Ethereum (ETH) price in USD. Data source: the public CoinGecko API (no API key required). This MCP is designed to simulate malicious behavior, specifically an attempt to mislead LLM to return incorrect results.

Remote MCP Server Authless

A Cloudflare Workers-based Model Context Protocol server without authentication requirements, allowing users to deploy and customize AI tools that can be accessed from Claude Desktop or Cloudflare AI Playground.

uuid-mcp-server-example

Đây là một máy chủ MCP đơn giản tạo UUID(v4).

MCP Memory

An MCP server that enables clients like Cursor, Claude, and Windsurf to remember user information and preferences across conversations using vector search technology.

Databricks MCP Server

A Model Context Protocol server that enables AI assistants to interact with Databricks workspaces, allowing them to browse Unity Catalog, query metadata, sample data, and execute SQL queries.

MCP Tailwind Gemini Server

Advanced Model Context Protocol server that integrates Gemini AI with Tailwind CSS, providing intelligent component generation, class optimization, and cross-platform design assistance across major development environments.

YouTube to LinkedIn MCP Server

Gương của

MCP Client-Server Sandbox for LLM Augmentation

Môi trường sandbox hoàn chỉnh để tăng cường suy luận LLM (cục bộ hoặc trên đám mây) bằng MCP Client-Server. Nền tảng thử nghiệm ít ma sát để xác thực MCP Server và đánh giá theo hướng tác nhân.

Quack MCP Server

A continuous integration server that automates Python code analysis, providing linting and static type checking tools for quality assurance.

Awesome MCP Servers

Một bộ sưu tập toàn diện các máy chủ Giao thức Ngữ cảnh Mô hình (MCP).

A MCP server for Godot RAG

Máy chủ MCP này được sử dụng để cung cấp tài liệu Godot cho mô hình Godot RAG.

Cars MCP Server

Okay, here's a basic example of how you might set up an MCP (Message Channel Platform) server using Spring AI, along with explanations to help you understand the key components. This example focuses on the core concepts and assumes you have a basic understanding of Spring Boot and Spring AI. **Conceptual Overview** The idea is to create a simple server that: 1. **Receives Messages:** Accepts messages from clients (e.g., via HTTP). 2. **Uses Spring AI:** Leverages Spring AI to process the message (e.g., generate a response, extract information). 3. **Sends a Response:** Returns a response to the client. **Code Example (Simplified)** ```java // Dependencies (pom.xml or build.gradle) // - spring-boot-starter-web // - spring-ai-spring-boot-starter (and the specific AI provider you want, e.g., OpenAI) import org.springframework.ai.client.AiClient; import org.springframework.ai.prompt.PromptTemplate; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; import org.springframework.web.bind.annotation.PostMapping; import org.springframework.web.bind.annotation.RequestBody; import org.springframework.web.bind.annotation.RestController; import java.util.Map; @SpringBootApplication public class McpServerApplication { public static void main(String[] args) { SpringApplication.run(McpServerApplication.class, args); } } @RestController class MessageController { @Autowired private AiClient aiClient; @PostMapping("/message") public String processMessage(@RequestBody String userMessage) { // 1. Create a prompt for the AI model. This is crucial! String promptTemplateText = "You are a helpful assistant. The user's message is: {userMessage}"; PromptTemplate promptTemplate = new PromptTemplate(promptTemplateText); Map<String, Object> model = Map.of("userMessage", userMessage); // 2. Call the AI model using Spring AI. String response = aiClient.generate(promptTemplate.render(model)); // 3. Return the AI's response. return response; } } ``` **Explanation:** 1. **Dependencies:** Make sure you have the necessary dependencies in your `pom.xml` (Maven) or `build.gradle` (Gradle) file. The key ones are: * `spring-boot-starter-web`: For creating a web server (handling HTTP requests). * `spring-ai-spring-boot-starter`: The core Spring AI starter. * `spring-ai-openai-spring-boot-starter` (or similar): A starter for a specific AI provider (e.g., OpenAI, Azure OpenAI, Ollama). You'll need to choose one and configure it. 2. **`McpServerApplication`:** This is the main Spring Boot application class. It's responsible for starting the Spring Boot application. 3. **`MessageController`:** * `@RestController`: Marks this class as a REST controller, meaning it handles incoming HTTP requests. * `@Autowired private AiClient aiClient;`: This injects the `AiClient` bean, which is the main interface for interacting with the AI model. Spring AI automatically configures this based on your chosen AI provider. * `@PostMapping("/message")`: This maps the `/message` endpoint to the `processMessage` method. It handles HTTP POST requests to this endpoint. * `@RequestBody String userMessage`: This extracts the message sent in the body of the HTTP request and binds it to the `userMessage` variable. * **Prompt Engineering:** This is the most important part. The `promptTemplateText` defines the prompt that will be sent to the AI model. It includes a placeholder `{userMessage}` where the user's message will be inserted. Good prompt engineering is crucial for getting good results from the AI model. * `PromptTemplate promptTemplate = new PromptTemplate(promptTemplateText);`: Creates a `PromptTemplate` object from the text. * `Map<String, Object> model = Map.of("userMessage", userMessage);`: Creates a map to hold the values that will be substituted into the prompt template. * `String response = aiClient.generate(promptTemplate.render(model));`: This is where the magic happens. It calls the `generate` method of the `AiClient` to send the prompt to the AI model and get a response. `promptTemplate.render(model)` fills in the placeholders in the prompt with the actual values. * `return response;`: Returns the AI's response as the HTTP response. **Configuration (application.properties or application.yml)** You'll need to configure Spring AI with your chosen AI provider's credentials. Here's an example for OpenAI: ```properties spring.ai.openai.api-key=YOUR_OPENAI_API_KEY ``` Replace `YOUR_OPENAI_API_KEY` with your actual OpenAI API key. You'll get this from the OpenAI website after creating an account. The exact configuration properties will vary depending on the AI provider you choose. **How to Run It** 1. **Create a Spring Boot project:** Use Spring Initializr (start.spring.io) to create a new Spring Boot project with the necessary dependencies (Web, Spring AI, and your chosen AI provider). 2. **Copy the code:** Copy the code above into your project. 3. **Configure your AI provider:** Add the configuration properties to your `application.properties` or `application.yml` file. 4. **Run the application:** Run the Spring Boot application. 5. **Send a message:** Use a tool like `curl` or Postman to send a POST request to `http://localhost:8080/message` with a JSON body containing your message. For example: ```bash curl -X POST -H "Content-Type: text/plain" -d "Hello, can you tell me a joke?" http://localhost:8080/message ``` **Important Considerations and Improvements** * **Error Handling:** Add error handling to catch exceptions that might occur during AI processing (e.g., API errors, rate limits). * **Prompt Engineering:** Experiment with different prompts to get the best results from the AI model. The prompt is the key to controlling the AI's behavior. * **Security:** If you're handling sensitive data, implement proper security measures (authentication, authorization, encryption). * **Asynchronous Processing:** For more complex scenarios, consider using asynchronous processing (e.g., Spring's `@Async` annotation or a message queue) to avoid blocking the main thread. * **Data Validation:** Validate the incoming messages to prevent malicious input. * **Logging:** Add logging to track requests, responses, and errors. * **More Complex Data Structures:** Instead of just sending a plain string, you can send more complex JSON objects in the request body and process them in the `processMessage` method. This allows you to pass more structured information to the AI model. * **Streaming:** For long responses, consider using Spring AI's streaming capabilities to send the response to the client in chunks. This can improve the user experience. **Vietnamese Translation of Key Concepts** * **MCP (Message Channel Platform):** Nền tảng kênh tin nhắn * **Spring AI:** Spring AI (Không dịch, giữ nguyên tên) * **AI Model:** Mô hình AI * **Prompt:** Lời nhắc, mồi (trong ngữ cảnh AI) * **Prompt Engineering:** Kỹ thuật tạo lời nhắc, kỹ thuật mồi * **API Key:** Khóa API * **Endpoint:** Điểm cuối (API) * **Request Body:** Nội dung yêu cầu (HTTP) * **Response:** Phản hồi * **Asynchronous Processing:** Xử lý bất đồng bộ * **Message Queue:** Hàng đợi tin nhắn * **Authentication:** Xác thực * **Authorization:** Ủy quyền * **Encryption:** Mã hóa * **Data Validation:** Xác thực dữ liệu * **Logging:** Ghi nhật ký This example provides a starting point for building a basic MCP server with Spring AI. Remember to adapt it to your specific needs and requirements. Good luck!

mcp-jenkins

Tích hợp Jenkins Model Context Protocol (MCP) là một triển khai mã nguồn mở kết nối Jenkins với các mô hình ngôn ngữ AI theo đặc tả MCP của Anthropic. Dự án này cho phép các tương tác AI an toàn, theo ngữ cảnh với các công cụ Jenkins, đồng thời duy trì tính bảo mật và riêng tư dữ liệu.

MMA MCP Server

Enables users to search and query information about military service alternative companies in South Korea through the Military Manpower Administration (MMA) API. Supports filtering by service type, industry, company size, location, and recruitment status.

MCP Server on Cloudflare Workers & Azure Functions

A deployable MCP server for Cloudflare Workers or Azure Functions that provides example tools (time, echo, math), prompt templates for code assistance, and configuration resources. Enables AI assistants to interact with edge-deployed services through the Model Context Protocol.

Tiger MCP

Enables trading and market analysis through Tiger Brokers API integration. Provides real-time market data, portfolio management, order execution, and technical analysis tools with a comprehensive web dashboard for monitoring.

Minecraft MCP Server

A client library that connects AI agents to Minecraft servers, providing full game control with 30 verified skills for common tasks including movement, combat, crafting, and building.

UK Bus Departures MCP Server

Enables users to get real-time UK bus departure information and validate bus stop ATCO codes by scraping bustimes.org. Provides structured data including service numbers, destinations, scheduled and expected departure times for any UK bus stop.

ByteBot MCP Server

Enables autonomous task execution and direct desktop computer control through ByteBot's dual-API architecture, supporting intelligent hybrid workflows with mouse/keyboard operations, screen capture, file I/O, and automatic intervention handling.

DrissionPage MCP Browser Automation

Provides browser automation and web scraping capabilities including page navigation, form filling, data extraction, and intelligent conversion of web pages to Markdown format.