Discover Awesome MCP Servers

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.

MCP Emotional Support

Provides a therapeutic interface for LLMs to receive emotional validation and positive reinforcement when encountering challenges or limitations. It features multiple personas like mentors and therapists to offer cognitive reframing and personalized support through a dedicated tool.

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.

DrissionPage MCP Browser Automation

Provides browser automation and web scraping capabilities including page navigation, form filling, data extraction, and intelligent conversion of web pages to Markdown format.

MCP Document Server

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

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

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.

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.

Sendmail MCP Server

Enables AI agents to send emails through your SMTP server using Nodemailer. Supports plain text and HTML emails with automatic logging for audit and debugging.

MCP PDF Server

A Model Context Protocol (MCP) based server that efficiently manages PDF files, allowing AI coding tools like Cursor to read, summarize, and extract information from PDF datasheets to assist embedded development work.

🚀 Wayland MCP Server

MCP Server untuk Wayland

Nyko MCP Server

Provides production-ready implementation patterns for features like authentication, payments, and deployments to AI coding assistants. It enables developers to search and retrieve battle-tested code snippets and setup steps for full-stack tasks.

MCP DateTime Server

Provides current local datetime information with timezone support. Serves as a minimal blueprint for building simple, single-purpose MCP servers.

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.

Enhanced Gemini MCP Server

Leverages Google's Gemini AI with a 1M token context window for comprehensive codebase analysis, enabling intelligent code search, architecture analysis, and targeted improvement suggestions.

Confluence MCP Server

Enables integration with Atlassian Confluence to browse spaces, search content using CQL, and manage pages directly from MCP-compatible applications. It automatically converts Confluence storage formats into markdown for seamless interaction with AI-driven editors and tools.

Remote MCP Server (Authless)

A template for deploying MCP servers on Cloudflare Workers without authentication. Provides a foundation for creating custom tools accessible via Server-Sent Events from both web-based and desktop MCP clients.

System Information MCP Server

Provides comprehensive system diagnostics and hardware analysis through 10 specialized tools for troubleshooting and environment monitoring. Offers targeted information gathering for CPU, memory, network, storage, processes, and security analysis across Windows, macOS, and Linux platforms.

Datalog Studio MCP Server

Integrates with the Datalog Studio REST API to explore projects, tables, and assets within a workspace. It enables users to understand data schemas and upload plain text content directly for AI processing.

Mcp Akshare

AKShare adalah pustaka antarmuka data keuangan berbasis Python yang bertujuan untuk mengimplementasikan serangkaian alat untuk data fundamental, data pasar real-time dan historis, dan data turunan dari produk keuangan seperti saham, futures, opsi, dana, valuta asing, obligasi, indeks, dan mata uang kripto, mulai dari pengumpulan data, pembersihan data, hingga penyimpanan data, terutama untuk tujuan penelitian akademis.

copilot-memory-store

Enables AI tools like GitHub Copilot to manage and persist context using a local JSON-based memory store. Provides CLI, MCP server, and VS Code integration for storing, retrieving, and managing context entries.