Discover Awesome MCP Servers

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.

MCP Perplexity Server

Provides AI-powered search, research, and reasoning capabilities through integration with Perplexity.ai, offering three specialized tools: general conversational AI, deep research with citations, and advanced reasoning.

MCP Document Server

A local development server that provides an interface for managing and accessing markdown documents using the Model Context Protocol (MCP).

Wonderland Editor MCP Plugin

원더랜드 에디터를 위한 MCP 서버 플러그인

Model Context Protocol (MCP) MSPaint App Automation

I can provide you with a conceptual outline and code snippets to illustrate how you might approach building a simple Model Context Protocol (MCP) server-client system for solving math problems and displaying the solution in MSPaint. However, directly controlling MSPaint programmatically can be tricky and might require external libraries or workarounds. This example focuses on the core MCP communication and a simplified way to visualize the solution (e.g., saving an image that can be opened in MSPaint). **Conceptual Outline:** 1. **MCP Server (Python):** * Listens for incoming client requests. * Receives a math problem (e.g., as a string). * Solves the problem (using libraries like `sympy` or `numpy`). * Generates a visual representation of the solution (e.g., using `matplotlib` to create a graph or equation). * Saves the visual representation as an image file (e.g., PNG). * Sends a response to the client, indicating success and the path to the image file. 2. **MCP Client (Python):** * Sends a math problem to the server. * Receives the server's response (success/failure and image path). * Opens the image file in MSPaint (using `os.startfile` on Windows or similar methods on other platforms). **Code Snippets (Python):** **MCP Server (server.py):** ```python import socket import sympy import matplotlib.pyplot as plt import io import base64 from PIL import Image import os HOST = '127.0.0.1' # Standard loopback interface address (localhost) PORT = 65432 # Port to listen on (non-privileged ports are > 1023) def solve_and_visualize(problem): """Solves the math problem and generates a visual representation.""" try: # Solve the problem using sympy x = sympy.symbols('x') equation = sympy.sympify(problem) solution = sympy.solve(equation, x) # Create a plot (example: plotting the equation) x_vals = range(-10, 11) y_vals = [sympy.lambdify(x, equation)(val) for val in x_vals] plt.plot(x_vals, y_vals) plt.xlabel("x") plt.ylabel("y") plt.title(f"Solution to: {problem}") plt.grid(True) # Save the plot to a file image_path = "solution.png" plt.savefig(image_path) plt.close() # Close the plot to free memory return True, image_path except Exception as e: print(f"Error solving/visualizing: {e}") return False, str(e) with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() print(f"Server listening on {HOST}:{PORT}") conn, addr = s.accept() with conn: print(f"Connected by {addr}") while True: data = conn.recv(1024) if not data: break problem = data.decode() print(f"Received problem: {problem}") success, result = solve_and_visualize(problem) if success: response = f"Success: {result}".encode() else: response = f"Error: {result}".encode() conn.sendall(response) ``` **MCP Client (client.py):** ```python import socket import os import platform HOST = '127.0.0.1' # The server's hostname or IP address PORT = 65432 # The port used by the server def open_with_mspaint(image_path): """Opens the image in MSPaint.""" try: if platform.system() == "Windows": os.startfile(image_path) # Windows elif platform.system() == "Darwin": # macOS os.system(f"open -a PaintBrush {image_path}") # Requires Paintbrush app else: # Linux (requires a suitable image viewer) os.system(f"xdg-open {image_path}") except Exception as e: print(f"Error opening MSPaint: {e}") with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.connect((HOST, PORT)) problem = input("Enter a math problem (e.g., x**2 - 4 = 0): ") s.sendall(problem.encode()) data = s.recv(1024) response = data.decode() print(f"Received: {response}") if "Success:" in response: image_path = response.split(": ")[1] open_with_mspaint(image_path) else: print("Problem solving failed.") ``` **Explanation and Improvements:** * **Libraries:** The code uses `socket` for network communication, `sympy` for symbolic math, `matplotlib` for plotting, and `os` for file operations and opening MSPaint. Install these using `pip install sympy matplotlib Pillow`. * **Error Handling:** Includes basic `try...except` blocks for error handling. More robust error handling is recommended. * **Visualization:** The `solve_and_visualize` function now creates a simple plot of the equation. You can customize this to display the solution in a more informative way (e.g., plotting the roots of the equation). It saves the plot as a PNG file. * **Image Handling:** The server saves the plot as a PNG file. The client receives the file path and attempts to open it with MSPaint. * **Platform Compatibility:** The `open_with_mspaint` function attempts to open the image using platform-specific commands. This is a basic approach; you might need to adjust it based on the user's operating system and available image viewers. On macOS, it uses `Paintbrush` which is a free MSPaint alternative. * **MCP Communication:** The code uses a simple text-based protocol. The server sends "Success: <image\_path>" or "Error: <error\_message>". You could use a more structured format like JSON for more complex data. * **Security:** This is a very basic example and lacks security features. In a real-world application, you would need to consider security implications, especially if you are accepting arbitrary math problems from clients. Sanitize inputs to prevent code injection. * **MSPaint Automation:** Directly controlling MSPaint programmatically is difficult. The `os.startfile` (Windows) or `os.system` (macOS/Linux) approach simply opens the image in MSPaint (or the default image viewer). For more advanced control, you might need to explore libraries like `pywinauto` (Windows) or platform-specific APIs. However, these can be complex. * **Threading/Asynchronous Operations:** For a production server, you would want to use threading or asynchronous operations to handle multiple client requests concurrently. **How to Run:** 1. **Save:** Save the server code as `server.py` and the client code as `client.py`. 2. **Install Libraries:** `pip install sympy matplotlib Pillow` 3. **Run the Server:** `python server.py` 4. **Run the Client:** `python client.py` 5. **Enter a Problem:** When prompted by the client, enter a math problem (e.g., `x**2 - 4 = 0`). 6. **View the Solution:** The client should receive a response from the server, and MSPaint (or the default image viewer) should open, displaying the solution. **Important Considerations:** * **MSPaint Automation Complexity:** Directly controlling MSPaint is challenging. The provided code opens the image in MSPaint, but you can't directly draw on it or manipulate it programmatically without significant effort and platform-specific code. * **Security:** Be very careful about accepting arbitrary math problems from clients, as this could pose a security risk. Sanitize inputs thoroughly. * **Error Handling:** Implement robust error handling to catch exceptions and provide informative error messages to the client. * **Scalability:** For a production system, consider using a more scalable architecture with threading or asynchronous operations to handle multiple clients concurrently. * **Alternative Visualization:** If you don't need to use MSPaint specifically, consider using other libraries like `PIL` (Pillow) to create and manipulate images directly in your Python code. This would give you more control over the image generation process. This revised response provides a more complete and practical example, along with important considerations for building a real-world MCP server-client system. Remember to adapt the code to your specific needs and environment.

MCP Firebird

Firebird SQL 데이터베이스를 위해 Anthropic의 모델 컨텍스트 프로토콜(MCP)을 구현하는 서버입니다. 이를 통해 Claude 및 다른 LLM이 자연어를 통해 Firebird 데이터베이스의 데이터에 안전하게 접근, 분석 및 조작할 수 있습니다.

Google Calendar MCP Server by CData

Google Calendar MCP Server by CData

😎 Contributing

🔥🔒 Awesome MCP (Model Context Protocol) Security 🖥️

Voice Call MCP Server

Claude와 같은 AI 어시스턴트가 Twilio와 OpenAI의 음성 모델을 사용하여 실시간 음성 통화를 시작하고 관리할 수 있도록 하는 모델 컨텍스트 프로토콜 서버.

Domain Availability Checker MCP

Domain Availability Checker MCP

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

UUID(v4)를 생성하는 간단한 MCP 서버입니다.

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.

Concordium MCP Server

Concordium mcp-sever for interacting with the concordium chain

LW MCP Agents

A lightweight framework for building and orchestrating AI agents through the Model Context Protocol, enabling users to create scalable multi-agent systems using only configuration files.

Image Process MCP Server

이미지 처리 기능을 제공하기 위해 Sharp 라이브러리를 사용하는 이미지 처리용 MCP 서버입니다.

Mobile Development MCP

이것은 모바일 기기 및 시뮬레이터를 관리하고 상호 작용하도록 설계된 MCP입니다.

🧠 MCP PID Wallet Verifier

AI 에이전트 또는 MCP 호환 어시스턴트가 OIDC4VP를 통해 PID (개인 식별 데이터) 자격 증명 제시를 시작하고 확인할 수 있도록 하는 가볍고 AI 친화적인 MCP 서버입니다.

Domain-MCP

A simple MCP server that enables AI assistants to perform domain research including availability checking, WHOIS lookups, DNS record retrieval, and finding expired domains without requiring API keys.

MCP Server with Azure Communication Services Email

Azure Communication Services - 이메일 MCP

Model Context Protocol (MCP) + Spring Boot Integration

새로운 MCP 서버 기능을 Spring Boot를 사용하여 테스트 중.

GLM-4.5V MCP Server

Enables multimodal AI capabilities through GLM-4.5V API for image processing, visual querying with OCR/QA/detection modes, and file content extraction from various formats including PDFs, documents, and images.

FastMCP Server Generator

사용자가 맞춤형 MCP 서버를 만들 수 있도록 돕는 전문 MCP 서버

Remote MCP Server Authless

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