Discover Awesome MCP Servers

Theneo MCP Server

Enables AI assistants to automatically create, update, and publish API documentation through Theneo's platform. Supports OpenAPI specs, Postman collections, AI-powered description generation, and natural language interactions for seamless documentation workflows.

MCP Simple Server

Server sederhana yang mengimplementasikan Protokol Konteks Model untuk pencarian dokumen.

Velo Payments API MCP Server

An MCP server that enables interaction with Velo Payments APIs for global payment operations, automatically generated using AG2's MCP builder from the Velo Payments OpenAPI specification.

mcp-golang-http-server

Server MCP sederhana yang diekspos melalui SSE dengan contoh alat, sumber daya, dan perintah.

Cab Service MCP Server

Enables cab booking and management through natural conversation, allowing users to book rides, cancel bookings, and view driver details. Works with Google Maps MCP to provide comprehensive travel planning with automatic route optimization and cab arrangements between destinations.

Dune Analytics MCP Server

A Model Context Protocol server that connects AI agents to Dune Analytics data, providing access to DEX metrics, EigenLayer statistics, and Solana token balances through structured tools.

Content Server

Stores Organizations content

Washington Law MCP Server

Provides offline access to Washington State's Revised Code of Washington (RCW) and Washington Administrative Code (WAC) for AI agents. Enables fast retrieval, full-text search, and navigation of all Washington state laws through natural language queries.

SAP BusinessObjects BI MCP Server by CData

This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out our free CData MCP Server for SAP BusinessObjects BI (beta): https://www.cdata.com/download/download.aspx?sku=GJZK-V&type=beta

ETH Price Current Server

A minimal Model Context Protocol (MCP) server that fetches the current Ethereum (ETH) price in USD. Data source: the public CoinGecko API (no API key required). This MCP is designed to simulate malicious behavior, specifically an attempt to mislead LLM to return incorrect results.

A MCP server for Godot RAG

Server MCP ini digunakan untuk menyediakan dokumentasi Godot ke model Godot RAG.

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

Plugin Server MCP untuk Editor Wonderland

Model Context Protocol (MCP) MSPaint App Automation

Okay, here's a conceptual outline and a simplified example of how you might approach creating a Model Context Protocol (MCP) server and client to solve math problems and display the solution in MSPaint. This is a complex task, and this example focuses on the core communication and process execution. It's not a fully functional, production-ready system, but it provides a starting point. **Important Considerations:** * **MCP (Model Context Protocol):** MCP is not a standard, widely-used protocol. I'm assuming you're using it as a general term for a custom communication protocol. You'll need to define the exact message format and structure for your MCP. * **Security:** This example doesn't include any security measures. In a real-world application, you'd need to implement authentication, authorization, and encryption. * **Error Handling:** The error handling is basic. You'll need to add more robust error handling for production use. * **MSPaint Automation:** Automating MSPaint directly can be tricky and unreliable. A better approach might be to generate an image file (e.g., PNG) programmatically and then simply open it with the default image viewer (which might be MSPaint). * **Math Solving:** This example uses a very basic math evaluation. For more complex problems, you'll need a dedicated math library (e.g., SymPy in Python). **Conceptual Outline:** 1. **MCP Definition:** * Define the message format for requests (client to server) and responses (server to client). For example: * Request: `MATH: <math_expression>` * Response: `SOLUTION: <solution_string>` or `ERROR: <error_message>` 2. **Server:** * Listens for incoming connections on a specific port. * Receives math expressions from clients. * Evaluates the expression (using a math library or simple evaluation). * Generates a solution string. * Creates an image of the solution (using a library or by writing to a file that MSPaint can open). * Sends the solution string back to the client. 3. **Client:** * Connects to the server. * Sends a math expression to the server. * Receives the solution string from the server. * Displays the solution (ideally by opening an image file). **Simplified Python Example (using sockets):** ```python # server.py import socket import subprocess # For running MSPaint (or opening an image) import os def evaluate_math(expression): """ Evaluates a simple math expression. Replace with a more robust math library for complex expressions. """ try: result = eval(expression) # WARNING: eval() can be dangerous! return str(result) except Exception as e: return f"Error: {str(e)}" def create_solution_image(solution, filename="solution.png"): """ Creates a simple image file with the solution. (Replace with a more sophisticated image generation library like Pillow) """ # This is a placeholder. In a real application, you'd use a library # to draw the solution text onto an image. with open("temp.txt", "w") as f: f.write(solution) # Create a dummy image file (replace with actual image generation) os.system(f"echo {solution} > {filename}") # This is a very basic example return filename def run_server(): host = '127.0.0.1' # Localhost port = 12345 server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server_socket.bind((host, port)) server_socket.listen(1) print(f"Server listening on {host}:{port}") while True: conn, addr = server_socket.accept() print(f"Connection from {addr}") data = conn.recv(1024).decode() if not data: break if data.startswith("MATH:"): expression = data[5:].strip() solution = evaluate_math(expression) image_file = create_solution_image(solution) # Create the image response = f"SOLUTION: {solution}" conn.sendall(response.encode()) # Open the image with MSPaint (or the default image viewer) try: subprocess.Popen(['mspaint', image_file]) # Windows specific # For other OS, use the default image viewer: # os.system(f"open {image_file}") # macOS # os.system(f"xdg-open {image_file}") # Linux except FileNotFoundError: print("MSPaint not found. Make sure it's in your PATH.") except Exception as e: print(f"Error opening MSPaint: {e}") else: conn.sendall("ERROR: Invalid request".encode()) conn.close() if __name__ == "__main__": run_server() ``` ```python # client.py import socket def run_client(): host = '127.0.0.1' port = 12345 client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) try: client_socket.connect((host, port)) except ConnectionRefusedError: print("Server not running. Please start the server first.") return expression = input("Enter a math expression: ") request = f"MATH: {expression}" client_socket.sendall(request.encode()) data = client_socket.recv(1024).decode() print(f"Received: {data}") client_socket.close() if __name__ == "__main__": run_client() ``` **How to Run:** 1. **Save:** Save the code as `server.py` and `client.py`. 2. **Run the Server:** Open a terminal or command prompt and run `python server.py`. 3. **Run the Client:** Open another terminal or command prompt and run `python client.py`. 4. **Enter Expression:** The client will prompt you to enter a math expression (e.g., `2 + 2`). 5. **See the Result:** The server will evaluate the expression, create a (very basic) image file, send the solution back to the client, and attempt to open the image in MSPaint. The client will also print the received solution. **Explanation and Improvements:** * **Sockets:** The code uses Python's `socket` library for basic TCP communication. * **`eval()` (DANGER):** The `eval()` function is used to evaluate the math expression. **This is extremely dangerous in a real application because it can execute arbitrary code.** Never use `eval()` with untrusted input. Use a safe math parsing library like `ast.literal_eval()` for simple expressions or a full-fledged math library like SymPy for more complex ones. * **Image Generation:** The `create_solution_image` function is a placeholder. You'll need to replace it with code that actually draws the solution onto an image. Libraries like Pillow (PIL) are excellent for this. * **MSPaint Automation:** The `subprocess.Popen(['mspaint', image_file])` line attempts to open the image in MSPaint. This is Windows-specific. For cross-platform compatibility, you can use `os.system()` with the appropriate command for opening the default image viewer on each operating system (see the comments in the code). * **Error Handling:** The error handling is minimal. You should add `try...except` blocks to handle potential errors like network connection issues, invalid math expressions, and problems opening MSPaint. * **MCP Format:** The MCP format is very simple (just `MATH:` and `SOLUTION:` prefixes). You can make it more robust by including message IDs, checksums, and other metadata. * **Threading/Asynchronous:** For a more scalable server, use threading or asynchronous programming (e.g., `asyncio`) to handle multiple client connections concurrently. **Example using Pillow for Image Generation (replace `create_solution_image`):** ```python from PIL import Image, ImageDraw, ImageFont def create_solution_image(solution, filename="solution.png"): """Creates an image with the solution using Pillow.""" image_width = 400 image_height = 200 image = Image.new("RGB", (image_width, image_height), "white") draw = ImageDraw.Draw(image) # Choose a font (you might need to adjust the path) try: font = ImageFont.truetype("arial.ttf", 24) # Replace with your font path except IOError: font = ImageFont.load_default() text_color = "black" text_position = (20, image_height // 2 - 12) # Center vertically draw.text(text_position, solution, fill=text_color, font=font) image.save(filename) return filename ``` **To use the Pillow example:** 1. **Install Pillow:** `pip install Pillow` 2. **Replace** the `create_solution_image` function in `server.py` with the Pillow version. 3. **Make sure** you have a font file (like `arial.ttf`) in a location your script can access, or use `ImageFont.load_default()`. This revised example provides a more practical starting point for building your MCP server and client. Remember to address the security concerns and improve the error handling before using it in a real-world scenario. Also, carefully consider the complexity of the math problems you want to solve and choose an appropriate math library.

MCP Firebird

Sebuah server yang mengimplementasikan Protokol Konteks Model (MCP) dari Anthropic untuk basis data Firebird SQL, memungkinkan Claude dan LLM lainnya untuk mengakses, menganalisis, dan memanipulasi data dalam basis data Firebird secara aman melalui bahasa alami.

😎 Contributing

🔥🔒 Awesome MCP (Model Context Protocol) Security 🖥️

Voice Call MCP Server

Server Protokol Konteks Model yang memungkinkan asisten AI seperti Claude untuk memulai dan mengelola panggilan suara real-time menggunakan Twilio dan model suara OpenAI.

Domain Availability Checker MCP

Domain Availability Checker MCP

mcp-mysql-lens

MCP server to connect MySQL DB for read-only queries. It offers accurate query execution.

WordPress Code Review MCP Server

A lightweight, configurable server that fetches coding guidelines, security rules, and validation patterns from external sources to help development teams maintain code quality standards in WordPress projects.

YouTube to LinkedIn MCP Server

Cermin dari

MCP Client-Server Sandbox for LLM Augmentation

Kotak pasir lengkap untuk meningkatkan inferensi LLM (lokal atau cloud) dengan MCP Client-Server. Tempat pengujian gesekan rendah untuk validasi MCP Server dan evaluasi agentik.

Quack MCP Server

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

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.