Discover Awesome MCP Servers

TOTP MCP Server

Generates time-based one-time password (TOTP) 2FA codes for configured accounts, enabling Claude to automate workflows requiring two-factor authentication.

de-eli-mcp

Searches and retrieves German federal legislation from NeuRIS, the official legal information portal, providing verifiable ELI identifiers and German citations.

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.

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.

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.

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.

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.

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.

Dovetail MCP Server

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

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.

claude-remind-mcp

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

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.

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.

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.

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.

ferric-fred-mcp

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

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.

Radar de Riesgo de Devolución

MCP server for e-commerce return risk analysis, providing tools to calculate customer risk profiles, compare segments, and identify risk factors, with memory for contextual conversations.

ytmcp

Enables AI assistants to fetch YouTube video transcripts with precise timestamps, multi-language support, and time-range filtering.

readypermit-mcp

AI-powered property intelligence for instant zoning analysis, buildability assessments, ADU eligibility, flood risk, and development feasibility reports for any US address.

IoT Device Management MCP Server

Enables registration, monitoring, and control of IoT devices via AI agents, with local storage and no cloud API key required.

phase8-mcp

MCP server for the Korg Phase 8 acoustic synthesizer that enables triggering resonators, controlling per-resonator knobs, and modulating global parameters over USB MIDI.

ellmos-servercommander-mcp

Alpha MCP server for server operations enabling deployment dry-runs, mail readiness diagnostics, access-log analysis, and HTTP health checks.

paraph-mcp

MCP server for the Paraph e-signature API that enables AI tools to fill PDF forms and manage electronic signing workflows. It provides tools for template management, document filling, sending signing requests, and tracking signing progress.

agentguard

Enables scanning of AI agent code for security vulnerabilities such as prompt injection, tool abuse, and data exfiltration, directly from MCP-compatible clients like Claude Code.

Amazon Product Search MCP

Enables AI-powered Amazon product searches and recommendations by integrating the Amazon API with Hugging Face models. It allows users to filter products by price and specific features to receive tailored shopping suggestions.

reddit-trends-mcp

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

discord-mcp-server

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

Cars MCP Server

Okay, here's a basic example of how you might set up a simple Minecraft Protocol (MCP) server using Spring AI. This is a high-level outline and requires you to fill in the details based on your specific needs and the MCP library you choose. This example focuses on the Spring AI integration for handling commands or interactions. **Important Considerations:** * **MCP Library:** There isn't a single "standard" MCP library for Java. You'll need to choose one. Popular options include: * **MinecraftForge:** A very common modding platform. If you're building a mod, this is likely your choice. * **SpongeAPI:** Another modding platform, known for its plugin API. * **Custom Implementation:** You *could* implement the MCP protocol yourself, but this is a significant undertaking. I strongly recommend using an existing library. * **Spring Boot:** This example assumes you're using Spring Boot for easy setup and dependency management. * **Spring AI:** This example uses Spring AI to process player input and generate responses. **Project Setup (Maven or Gradle):** Add the following dependencies to your `pom.xml` (Maven) or `build.gradle` (Gradle): **Maven (`pom.xml`):** ```xml <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>0.8.0</version> <!-- Or the latest version --> </dependency> <dependency> <groupId>org.springframework.ai</groupId> <artifactId>spring-ai-openai</artifactId> <version>0.8.0</version> <!-- Or the latest version --> </dependency> <!-- Your chosen MCP library dependency goes here. Example using a hypothetical MCP library: --> <!-- <dependency> <groupId>com.example</groupId> <artifactId>mcp-library</artifactId> <version>1.0.0</version> </dependency> --> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> <scope>test</scope> </dependency> </dependencies> ``` **Gradle (`build.gradle`):** ```gradle dependencies { implementation 'org.springframework.boot:spring-boot-starter-web' implementation 'org.springframework.ai:spring-ai-core:0.8.0' // Or the latest version implementation 'org.springframework.ai:spring-ai-openai:0.8.0' // Or the latest version // Your chosen MCP library dependency goes here. Example using a hypothetical MCP library: // implementation 'com.example:mcp-library:1.0.0' testImplementation 'org.springframework.boot:spring-boot-starter-test' } ``` **1. Spring Boot Application Class:** ```java import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; @SpringBootApplication public class McpServerApplication { public static void main(String[] args) { SpringApplication.run(McpServerApplication.class, args); } } ``` **2. MCP Server Component (Example):** ```java import org.springframework.ai.client.AiClient; import org.springframework.ai.prompt.PromptTemplate; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.beans.factory.annotation.Value; import org.springframework.stereotype.Component; import javax.annotation.PostConstruct; import javax.annotation.PreDestroy; import java.util.HashMap; import java.util.Map; @Component public class McpServer { // Replace with your actual MCP server implementation private boolean isRunning = false; @Autowired private AiClient aiClient; @Value("${spring.ai.openai.api-key}") private String openAiApiKey; @Value("${mcp.server.port}") private int serverPort; @Value("${mcp.ai.prompt}") private String aiPrompt; @PostConstruct public void startServer() { System.out.println("Starting MCP Server on port: " + serverPort); System.out.println("Using OpenAI API Key: " + openAiApiKey); // Initialize your MCP server here (using your chosen library) // Example (replace with actual code): // this.mcpServer = new MyMcpserver(serverPort); // this.mcpServer.start(); isRunning = true; System.out.println("MCP Server started."); } @PreDestroy public void stopServer() { if (isRunning) { System.out.println("Stopping MCP Server"); // Stop your MCP server here (using your chosen library) // Example (replace with actual code): // this.mcpServer.stop(); isRunning = false; System.out.println("MCP Server stopped."); } } // Example method to handle player input and use Spring AI public String handlePlayerCommand(String playerName, String command) { System.out.println("Received command from " + playerName + ": " + command); // Use Spring AI to generate a response PromptTemplate promptTemplate = new PromptTemplate(aiPrompt); Map<String, Object> model = new HashMap<>(); model.put("playerName", playerName); model.put("command", command); String response = aiClient.generate(promptTemplate.create(model)).getGeneration().getText(); System.out.println("AI Response: " + response); return response; // Or send the response back to the player in-game } } ``` **3. Configuration (`application.properties` or `application.yml`):** ```properties spring.ai.openai.api-key=YOUR_OPENAI_API_KEY # Replace with your actual OpenAI API key mcp.server.port=25565 # Or your desired port mcp.ai.prompt=Player {playerName} issued command: {command}. Respond in a helpful and Minecraft-themed way. ``` **Explanation:** * **Dependencies:** The `spring-boot-starter-web` dependency is included for basic web functionality (though you might not need it directly for the MCP server itself, it's often useful for management endpoints). `spring-ai-core` and `spring-ai-openai` are the core Spring AI dependencies. You'll need to add the dependency for your chosen MCP library. * **`McpServerApplication`:** A standard Spring Boot application entry point. * **`McpServer` Component:** * `@Component`: Marks this class as a Spring-managed component. * `@Autowired AiClient`: Injects the Spring AI client. * `@Value`: Injects values from your `application.properties` or `application.yml` file. **Important:** Replace `YOUR_OPENAI_API_KEY` with your actual OpenAI API key. * `@PostConstruct`: The `startServer()` method is called after the Spring context is initialized. This is where you would start your MCP server. **You'll need to replace the placeholder comments with the actual code to initialize and start your chosen MCP library.** * `@PreDestroy`: The `stopServer()` method is called when the Spring context is shutting down. This is where you would stop your MCP server. **You'll need to replace the placeholder comments with the actual code to stop your chosen MCP library.** * `handlePlayerCommand()`: This is a *very* simplified example of how you might handle player input. It takes the player's name and command as input, uses Spring AI to generate a response, and then returns the response. **You'll need to adapt this to your specific MCP library and how it handles player input.** * **Spring AI Integration:** * `PromptTemplate`: Defines the prompt that will be sent to the AI model. The prompt includes placeholders for the player's name and command. * `aiClient.generate()`: Sends the prompt to the AI model and returns a response. * The response is then printed to the console and returned. * **`application.properties`:** Contains the configuration for your application, including the OpenAI API key, the server port, and the AI prompt. **Remember to replace `YOUR_OPENAI_API_KEY` with your actual key.** **How to Use It (Conceptual):** 1. **Choose an MCP Library:** Select the MCP library that best suits your needs (MinecraftForge, SpongeAPI, or a custom implementation). 2. **Implement MCP Server Logic:** Replace the placeholder comments in the `McpServer` class with the actual code to initialize, start, and stop your MCP server using your chosen library. This will involve handling network connections, player authentication, world loading, etc. 3. **Handle Player Input:** Modify the `handlePlayerCommand()` method to receive player input from your MCP server. This will likely involve listening for specific events or packets from the MCP library. 4. **Send Responses to Players:** Modify the `handlePlayerCommand()` method to send the AI-generated response back to the player in the game. This will involve using the appropriate methods from your MCP library to send messages to players. 5. **Configure Spring AI:** Make sure you have a valid OpenAI API key and that you've configured it in your `application.properties` file. You can also experiment with different AI models and prompt templates to get the desired behavior. **Example Scenario:** 1. A player types `/ask what is the best way to find diamonds?` in the game. 2. Your MCP server receives this command. 3. The `handlePlayerCommand()` method is called with `playerName` set to the player's name and `command` set to "what is the best way to find diamonds?". 4. The `PromptTemplate` is used to create a prompt like: "Player Steve issued command: what is the best way to find diamonds?. Respond in a helpful and Minecraft-themed way." 5. The prompt is sent to the OpenAI API. 6. The OpenAI API generates a response, such as: "Ahoy, matey! To find diamonds, ye should dig down to level -58 and look for them near lava pools. Be careful, though, or ye might get burned!" 7. The response is sent back to the player in the game. **Important Notes:** * **Error Handling:** This is a very basic example and doesn't include any error handling. You'll need to add error handling to your code to make it more robust. * **Security:** Be very careful about security when building an MCP server. Make sure you properly authenticate players and protect against exploits. * **Asynchronous Operations:** MCP servers are typically multi-threaded. Make sure you handle player input and AI responses asynchronously to avoid blocking the main server thread. Consider using Spring's `@Async` annotation or other concurrency mechanisms. * **Rate Limiting:** Be mindful of the OpenAI API's rate limits. You may need to implement rate limiting in your code to avoid being throttled. * **Prompt Engineering:** The quality of the AI's responses depends heavily on the prompt you provide. Experiment with different prompts to get the best results. * **Cost:** Using OpenAI's API incurs costs. Be aware of the pricing and monitor your usage. **Chinese Translation of Key Terms:** * **MCP (Minecraft Protocol):** Minecraft 协议 (Minecraft Xiéyì) * **Spring AI:** Spring 人工智能 (Spring Réngōng Zhìnéng) * **Server:** 服务器 (Fúwùqì) * **Player:** 玩家 (Wánjiā) * **Command:** 命令 (Mìnglìng) * **Prompt:** 提示 (Tíshì) * **API Key:** API 密钥 (API Mìyuè) * **Dependency:** 依赖 (Yīlài) * **Configuration:** 配置 (Pèizhì) * **Response:** 回应 (Huíyìng) / 响应 (Xiǎngyìng) This example provides a starting point for building an MCP server with Spring AI. You'll need to adapt it to your specific needs and the MCP library you choose. Remember to consult the documentation for your chosen MCP library and the Spring AI documentation for more information. Good luck!

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.