Discover Awesome MCP Servers

PayHub MCP Server

Enables querying real disclosed salary data across 20 regions, with tools to search jobs, retrieve salary statistics, and find similar roles.

Wedding Invitation Builder MCP

Enables creating and customizing mobile wedding invitations through natural language, with support for multiple designs, RSVP, maps, gallery, and share tokens.

livespace-crm-mcp

Unofficial MCP server for Livespace CRM, exposing 11 intent-shaped tools for safe, bounded read and write operations on records, deals, activities, and notifications via Streamable HTTP.

SwiftOpenAI MCP Server

A universal server that enables MCP-compatible clients (like Claude Desktop, Cursor, VS Code) to access OpenAI's APIs for chat completions, image generation, embeddings, and model listing through a standardized interface.

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!

discord-mcp-server

Lets any MCP-compatible AI client interact with Discord — send messages, manage channels, create webhooks, assign roles, and more.

MCP Server for Odoo

Enables AI assistants to interact with Odoo ERP systems through natural language, allowing users to search, create, update, and manage business records like customers, products, and invoices across any Odoo instance.

wows-remote-agent

MCP server to remotely monitor and control a Windows PC running World of Warships via Tailscale, enabling status checks, screenshots, game launch, and calibrated menu workflows with safety limits.

unstuck-mcp

Prevents coding agents from repeatedly attempting the same failed fix by tracking attempts and blocking further fixes until the agent uses its own web search tool.

A MCP server for Godot RAG

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

Commodore 64 Ultimate MCP Server

Enables AI assistants to control Commodore 64 Ultimate hardware via REST API, supporting program execution, memory operations, disk management, audio playback, and device configuration through natural language commands.

reddit-trends-mcp

Provides Reddit discussion volume trends, growth rates, and top trending topics for any keyword, accessible via MCP tools and Python client.

mcp-guard

Zero-dependency local proxy that wraps any MCP server to redact secrets, strip hidden-Unicode prompt injection, and block writes to protected paths like ~/.ssh and .env.

FFmpeg MCP

Enables video and audio processing through FFmpeg, supporting format conversion, compression, trimming, audio extraction, frame extraction, video merging, and subtitle burning through natural language commands.

ferric-fred-mcp

A strongly-typed, single-binary MCP server for FRED (Federal Reserve Economic Data), written in Rust.

HuntX

MCP server providing direct tool-access to security-testing primitives for bug bounty hunting, including recon, request replay, IDOR/BOLA fuzzing, vulnerability detection, secrets scanning, and persistent hunt memory with confidence-scored findings.

Mandados de Prisão (CNJ)

Enables checking for open arrest warrants in the Brazilian CNJ national database using a person's CPF and name, via a hosted read-only MCP server.

Knowi-mcp

Knowi’s MCP server gives AI tools full access to the entire analytics workflow. Knowi's 20+ specialized data agents automatically chain together to connect you datasources, write queries, build dashboards, and deliver reports.

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.

Excel MCP Server

Enables conversational data analysis of Excel/CSV files through natural language queries, powered by 395 Excel functions via HyperFormula and multi-provider AI. Supports advanced analytics, bulk operations, financial modeling, and large file processing with intelligent chunking.

Red-team-mcp

An MCP server for red teaming that enables AI agents to perform port scanning, vulnerability scanning, SSH operations, and Metasploit exploitation through a unified interface.

claude-remind-mcp

Searches your local Claude Code conversation history to recall and resume past solutions.

Seq MCP Server

MCP server for querying structured logs from Datalust Seq, providing tools to search logs, retrieve recent errors, fetch events, and check health.

Dovetail MCP Server

Enables AI tools to connect to the Dovetail API for accessing customer insights and research data.

Purple Flea Wallet

Non-custodial HD wallet API for AI agents. Generate wallets on 6 chains (ETH, Base, SOL, BTC, TRX, XMR), check balances, send crypto, and swap cross-chain via Wagyu aggregator. 10% referral commissions.

Kolosal Vision MCP

Provides AI-powered image analysis and OCR capabilities using the Kolosal Vision API. Supports analyzing images from URLs, local files, or base64 data with natural language queries for object detection, scene description, text extraction, and visual assessment.

Hermai MCP

Enables agent runtimes to look up, classify, and fetch Hermai schemas as MCP tools, including read-only data retrieval via hosted endpoints (with optional API key).

mcp-proxy

A self-hosted, OAuth-fronted MCP proxy that lets Claude custom connectors reach RapidAPI's MCP endpoints by injecting API credentials, with per-upstream tool filtering and rate limiting.

personality-test-mcp

Enables AI models to administer personality tests, score responses, and provide personality type assessments, with optional integration with Ollama for personalized AI interactions.