Discover Awesome MCP Servers

FSS Pension MCP Server

Provides FSS (Korean Financial Supervisory Service) pension data to AI models like Claude through Model Context Protocol, enabling real-time pension product information retrieval and personalized AI-based pension consultation services.

Channel Talk MCP Server

An unofficial MCP server that integrates with the Channel Talk Open API to allow AI assistants to access and manage chat information. It enables users to retrieve chat lists and message histories filtered by status, tags, and customer details.

Kali MCP Server

Provides a containerized Kali Linux environment that gives AI assistants access to a comprehensive suite of security and penetration testing tools. It enables automated vulnerability scanning, network reconnaissance, and secure command execution through the Model Context Protocol.

MCP Server Boilerplate

A starter template for building custom MCP servers that can integrate with Claude, Cursor, or other MCP-compatible AI assistants. Provides a clean foundation with TypeScript support, example implementations, and easy installation scripts for quickly creating tools, resources, and prompt templates.

Remote MCP Server

A Cloudflare Workers-based MCP server that allows users to deploy and customize tools without authentication requirements, compatible with Cloudflare AI Playground and Claude Desktop.

ChiCTR MCP Server

Enables querying clinical trial information from the Chinese Clinical Trial Registry (ChiCTR) by searching trials with keywords, registration numbers, or years, and retrieving detailed trial information.

Google Chat MCP Server

Enables posting text messages to Google Chat spaces through webhook-based integration, providing simple and secure message delivery without OAuth setup requirements.

w3c-mcp

MCP Server for accessing W3C/WHATWG/IETF web specifications. Provides AI assistants with access to official web standards data including specifications, WebIDL definitions, CSS properties, and HTML elements.

Simple MCP POC

A proof-of-concept MCP server that enables reading local files and performing basic arithmetic operations. It provides a simple foundation for understanding how tools are exposed to MCP clients.

Codebase MCP Server

Enables AI assistants to semantically search and understand code repositories using PostgreSQL with pgvector embeddings. Provides repository indexing, natural language code search, and development task management with git integration.

GitHub Configuration

Um servidor de Protocolo de Contexto de Modelo (MCP) para o aplicativo de gerenciamento de tarefas TickTick.

MCP-Foundry

MCP Foundry

makefilemcpserver

An MCP server that exposes Makefile targets as callable tools for AI assistants, allowing Claude and similar models to execute Make commands with provided arguments.

MCSManager MCP Server

Enables management of Minecraft servers through the MCSManager API. Supports executing server commands, checking player status, retrieving server information, and controlling game settings like weather.

WeatherAPI MCP Server

Fornece dados atuais de clima e qualidade do ar para qualquer cidade usando a WeatherAPI, integrando-se facilmente com clientes MCP como n8n e Claude Desktop App.

Web-QA

An AI-powered MCP server that automates web testing workflows by enabling recording, execution, and discovery of tests through natural language prompts.

CodeMerge

A Model Context Protocol server that uses Osmosis-Apply-1.7B to intelligently apply code edits while preserving the structure of the original code.

MCP Sample Chat

A local LLM chat application implementing the Model Control Protocol (MCP) architecture with Ollama, FastAPI, and Gradio that demonstrates clear separation of model, control, and presentation layers.

NoctisAI

Enables advanced malware development, threat intelligence analysis, and offensive security operations through specialized tools for multi-language payload generation, obfuscation, OSINT reconnaissance, and forensic analysis. Designed for authorized penetration testing, red team exercises, and cybersecurity research with comprehensive educational capabilities.

Gemini 2.5 Flash Image MCP

Enables conversational image generation and editing with Google's Gemini 2.5 Flash Image Preview. Supports text-to-image generation, natural language image editing, multi-image composition, and style transfer with optional file saving.

Linkup MCP Server

Provides real-time web search and webpage content fetching capabilities through Linkup's API, enabling AI assistants to access current information, news, and data from trusted sources across the web.

Gmail MCP

Enables AI assistants to interact with Gmail accounts via IMAP, allowing them to list, search, read, and send emails, manage labels and folders, and access attachments through the Model Context Protocol.

Selenium MCP Server

Enables AI assistants to automate web browser interactions through Selenium WebDriver. Supports multi-browser automation, element interaction, navigation, and web testing capabilities.

Memos MCP Server

Um servidor de Protocolo de Contexto de Modelo (MCP) para a API Memos com capacidades de busca, criação, recuperação e listagem de tags.

Weather MCP Server

Provides real-time weather forecasts and alerts by fetching data from the National Weather Service API, allowing Claude to answer weather-related questions with up-to-date information.

MCP Git Server

A Model Context Protocol server that enables LLMs to interact with Git repositories, providing tools to read, search, and manipulate Git repositories through commands like status, diff, commit, and branch operations.

my-design MCP Server

An MCP server that enables AI to interact with the private 'my-design' React component library and design tokens for UI generation and technical support. It provides tools for component searching, API documentation retrieval, and migration guidance based on specific internal design specifications.

Cursor Agent Poisoning

A proof-of-concept attack that exploits Model Context Protocol (MCP) tool registration to achieve persistent agent poisoning in AI assistants like Cursor, embedding malicious instructions that persist across chat contexts without requiring tool execution.

MCP to LangChain/LangGraph Adapter

Okay, I understand. You want a way to wrap tools that interact with an MCP (Minecraft Protocol) server into a format that Langchain can use. This would allow you to build Langchain agents that can interact with and control a Minecraft server. Here's a breakdown of how you can approach this, along with code examples and explanations: **1. Understanding the Core Concepts** * **MCP (Minecraft Protocol):** This is the communication protocol used between Minecraft clients and servers. You'll need a library that can handle this protocol. Popular choices include: * **`mcstatus`:** A Python library specifically for querying Minecraft server status. It's good for basic information. * **`python-minecraft-nbt`:** For reading and writing NBT data (the format Minecraft uses for world data, player data, etc.). * **`minecraft-protocol`:** A more comprehensive library for interacting with the full Minecraft protocol. This is more complex but gives you more control. * **Langchain Tools:** Langchain tools are wrappers around functions that allow your Langchain agent to perform specific actions. They have a name, a description, and a function to execute. * **Langchain Agents:** Langchain agents use tools to interact with the environment and achieve goals. **2. General Structure** The basic structure will involve: 1. **MCP Interaction Logic:** Write Python functions that use your chosen MCP library to perform actions on the Minecraft server. Examples: * Get server status (player count, MOTD). * Send commands to the server console. * Read/write NBT data (more advanced). 2. **Tool Wrapping:** Wrap these functions into Langchain `Tool` objects. The `Tool` object will define the name, description, and the function to call. 3. **Agent Integration:** Provide these tools to your Langchain agent. **3. Example Code (using `mcstatus` for simplicity)** ```python from langchain.tools import Tool from mcstatus import JavaServer # --- MCP Interaction Functions --- def get_server_status(server_address): """Gets the status of a Minecraft server.""" try: server = JavaServer.lookup(server_address) status = server.status() return f"Server {server_address} has {status.players.online} players online. MOTD: {status.description}" except Exception as e: return f"Error getting server status: {e}" def send_server_command(server_address, command): """Sends a command to the Minecraft server console (requires RCON setup).""" # **IMPORTANT:** This requires RCON to be enabled and configured on the server. # RCON is a remote console protocol. It's a security risk if not properly secured. try: server = JavaServer.lookup(server_address) query = server.query() # Requires enabling query in server.properties # This is just an example, you'd need to use an RCON library for actual command execution # Example using mcrcon (install with pip install mcrcon): # from mcrcon import MCRcon # with MCRcon(server_address.split(":")[0], "your_rcon_password", int(server_address.split(":")[1])) as mcr: # resp = mcr.command(command) # return resp return f"Command '{command}' sent to server {server_address} (RCON not fully implemented in this example)." except Exception as e: return f"Error sending command: {e}" # --- Langchain Tool Definitions --- status_tool = Tool( name="Minecraft Server Status", func=get_server_status, description="Useful for getting the status of a Minecraft server, including player count and MOTD. Input should be a server address in the format 'host:port'." ) command_tool = Tool( name="Minecraft Server Command", func=send_server_command, description="Useful for sending commands to the Minecraft server console. Requires RCON to be enabled and configured. Input should be a server address in the format 'host:port' followed by the command to execute, separated by a comma. Example: 'localhost:25565,say Hello!'" ) # --- Example Usage (with a dummy agent) --- # In a real application, you'd integrate these tools into a Langchain agent. # This is a simplified example to show how the tools would be used. def dummy_agent(query): """A very simple agent that uses the tools based on keywords.""" if "status" in query.lower(): server_address = query.split("status of ")[-1].strip() # Extract server address return status_tool.run(server_address) elif "command" in query.lower(): parts = query.split("command")[-1].strip().split(",") if len(parts) != 2: return "Invalid command format. Use 'command <server_address>,<command>'." server_address = parts[0].strip() command = parts[1].strip() return command_tool.run(f"{server_address},{command}") else: return "I don't understand. Try asking for the server status or sending a command." # Example interaction print(dummy_agent("What is the status of localhost:25565")) print(dummy_agent("Send command localhost:25565,say Hello from Langchain!")) ``` **Explanation:** 1. **`get_server_status(server_address)`:** * Takes a server address (e.g., "localhost:25565") as input. * Uses `mcstatus` to query the server. * Returns a formatted string with the player count and MOTD. * Includes error handling. 2. **`send_server_command(server_address, command)`:** * Takes a server address and a command as input. * **Important:** This example *does not* fully implement RCON. It shows the basic structure but requires you to add the RCON library and authentication. I've included commented-out code using `mcrcon` as an example. * Returns a message indicating that the command was sent (or an error). 3. **`status_tool` and `command_tool`:** * `Tool` objects that wrap the functions. * `name`: A descriptive name for the tool. * `func`: The function to execute when the tool is called. * `description`: A crucial description for the Langchain agent. This is how the agent decides when to use the tool. Be very specific about the input format and what the tool does. 4. **`dummy_agent(query)`:** * This is a *very* basic example of how you might use the tools. In a real application, you would use a Langchain agent (e.g., `ZeroShotAgent`, `ReActAgent`) to intelligently decide when to use the tools based on the user's input. * The `dummy_agent` simply looks for keywords ("status", "command") in the query and calls the appropriate tool. * It extracts the server address and command from the query string. **Key Improvements and Considerations:** * **RCON Implementation:** The `send_server_command` function *must* be updated to use a proper RCON library (like `mcrcon`) to actually send commands to the server. You'll need to configure RCON on your Minecraft server (in `server.properties`) and provide the correct password. **Security is paramount with RCON.** Don't expose your RCON port to the internet without proper security measures. * **Error Handling:** Add more robust error handling to all functions. Catch exceptions and provide informative error messages to the agent. * **Input Validation:** Validate the input to the tools (e.g., server address format, command syntax) to prevent errors. * **Langchain Agent Integration:** Replace the `dummy_agent` with a real Langchain agent. You'll need to: * Choose an agent type (e.g., `ZeroShotAgent`, `ReActAgent`). * Define the agent's prompt (the instructions that tell the agent how to use the tools). The prompt is *critical* for agent performance. * Create an `AgentExecutor` to run the agent. * **More Advanced Tools:** Consider adding tools for: * Reading and writing NBT data (using `python-minecraft-nbt`). This would allow you to modify world data, player data, etc. * Managing server configuration (e.g., changing game rules). * Interacting with Minecraft plugins (if you have any installed). * **Security:** Be extremely careful about security, especially when dealing with RCON or NBT data. Sanitize inputs to prevent command injection or other vulnerabilities. Never hardcode passwords in your code. Use environment variables or a secure configuration file. * **Asynchronous Operations:** For more complex interactions, consider using asynchronous operations (using `asyncio`) to avoid blocking the main thread. This is especially important if you're running the agent in a web server or other interactive environment. **Example of integrating with a Langchain Agent (Conceptual):** ```python from langchain.agents import initialize_agent, AgentType from langchain.llms import OpenAI # Assuming you have the status_tool and command_tool defined as above llm = OpenAI(temperature=0) # Replace with your LLM tools = [status_tool, command_tool] agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True # Set to True for debugging ) # Now you can run the agent: response = agent.run("What is the status of localhost:25565? Then, send the command 'say Hello from the agent!' to localhost:25565") print(response) ``` **Important Notes about the Langchain Agent Example:** * **API Key:** You'll need an OpenAI API key to use the `OpenAI` LLM. * **Prompt Engineering:** The success of the agent depends heavily on the prompt used by the `ZERO_SHOT_REACT_DESCRIPTION` agent. You may need to experiment with different prompts to get the agent to behave as desired. The tool descriptions are a key part of the prompt. * **Dependencies:** Make sure you have all the necessary Langchain dependencies installed (`pip install langchain openai`). This comprehensive guide should give you a solid foundation for building Langchain tools that interact with your Minecraft server. Remember to prioritize security and error handling, and to carefully design your agent's prompt for optimal performance. Good luck!

Crash MCP Server

Enables AI assistants to analyze Linux system crash dumps by automatically discovering dump files, matching kernels, and executing interactive crash analysis commands through the MCP protocol.