Discover Awesome MCP Servers

Concordium MCP Server

Concordium mcp-sever for interacting with the concordium chain

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

Developer Research MCP Server

Provides structured web search capabilities optimized for technical and software development content via providers like OpenRouter. It enables AI agents to perform research and retrieve relevant technical data in a consistent, programmatic JSON format.

mcp-kubernetes-ro

Provides read-only access to Kubernetes clusters for AI assistants.

Weather MCP Server

Provides real-time weather information for 12 major Chinese cities and global locations using the wttr.in API. Built with the HelloAgents framework, it requires no API keys and supports queries in both Chinese and English.

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.

Sassy Fact Check Bot

Generates witty, citation-backed responses to health myths and misinformation with automatic tone adjustment for sensitive topics. Integrates with Instagram DMs to fact-check viral claims with sass and sources.

😎 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.

EduChain MCP Server

Integrates the EduChain library with Claude Desktop to generate educational content such as multiple-choice questions, lesson plans, and flashcards. It utilizes Gemini LLMs through LangChain to provide local and secure content generation tools.

MCP-Odoo

A bridge that allows AI agents to access and manipulate Odoo ERP data through a standardized Model Context Protocol interface, supporting partner information, accounting data, financial records reconciliation, and invoice queries.

EliteMCP

Analyzes directory structures with .gitignore awareness and executes Python code in secure sandboxed environments. Combines intelligent codebase analysis with safe code execution for development workflows.

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.

Domain Availability Checker MCP

Domain Availability Checker MCP

Vaiz MCP

Connects Cursor/Claude to your Vaiz workspace, enabling search and management of tasks, projects, documents, milestones, and team members through natural language.

YouTube to LinkedIn MCP Server

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Android MCP Server

Enables control and automation of Android devices using uiautomator2 and ADB. It supports UI interactions, app management, and shell command execution through the Model Context Protocol.

MCP Client-Server Sandbox for LLM Augmentation

LLM 추론(로컬 또는 클라우드)을 MCP 클라이언트-서버로 확장하기 위한 완벽한 샌드박스입니다. MCP 서버 검증 및 에이전트 평가를 위한 낮은 마찰의 테스트베드입니다.

img-gen

Provides tools for generating optimized images via Google's Gemini model and fetching weather forecasts and alerts from the National Weather Service. It enables users to create visual content and retrieve environmental data seamlessly within MCP-compatible clients.

Quack MCP Server

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

Agent Collaboration MCP Server

Enables AI agents to orchestrate a team of sub-agents through tmux sessions for complex task delegation and parallel implementation. It provides tools for launching agents, monitoring their real-time status, and managing communication between them.

Email Sender MCP Server

Enables sending emails through SMTP with support for multiple recipients, attachments, CC/BCC, and both plain text and HTML formats. Includes preset configurations for common email providers like Gmail, QQ, Outlook, and 163.

MCP Hub Database Server

Enables querying and searching the MCP Hub database to discover MCP servers, view server details, find top servers by popularity or recency, and identify top contributors.

TigerData-mcp-server

Run queries and pull information about your TigerData Cloud's PostgreSQL databases

Vault MCP Server

Enables interaction with HashiCorp Vault to read, write, list, and delete secrets through a containerized MCP server with secure token-based authentication.

web-monitor-mcp-safepoint

The mcp server for safepoint web monitor

OpenTargets MCP Server

Unofficial Model Context Protocol server for accessing Open Targets platform data for gene-drug-disease associations research.