Discover Awesome MCP Servers

Weather Prediction MCP Server

An MCP server that provides weather forecast tools (current weather, forecast, travel recommendations, and city comparison) powered by Open-Meteo, designed for Databricks Agent Bricks.

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.

i1n

Localization as code — push, pull, translate, and extract strings from code with AI. 7 MCP tools for type-safe i18n across 182 languages.

supertonic3-mcp

Enables local text-to-speech synthesis for Claude and Cursor using Supertonic 3, with support for multiple voices, expressions, and languages. No API key or cloud required.

agent-bus

Enables multiple coding agents (Claude Code, Codex, Cursor) to discover each other's sessions, search transcripts, ask questions, and handoff tasks through a shared MCP server.

touchdesigner-mcp

An MCP server for TouchDesigner that lets you control TouchDesigner with Claude

prelaunch-mcp

Analyzes startup ideas against 6 sources (GitHub, HN, npm, PyPI, Google, Reddit) with LLM-powered intent parsing to assess competition, demand, and gaps.

Somnia MCP Server

Enables AI agents to interact with the Somnia blockchain network, including documentation search, blockchain queries, wallet management, cryptographic signing, and on-chain operations.

transcribeMCP

MCP server for GovTech's Transcribe speech-to-text service, enabling audio upload, batch transcription, summaries, minutes, sections, notes, and transcript Q&A.

bq_mcp_server

A Python MCP server that retrieves and caches BigQuery metadata (datasets, tables, columns) and enables secure SQL query execution with cost control, file export, and keyword search.

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.

onyx-paid-mcp

Build a paid MCP server that charges AI agents per call in USDC, with automatic payment handling via HTTP 402 and EIP-3009.

Mavis MCP Server

Exposes the Mavis multi-agent system as an MCP server, enabling Claude Code and other MCP clients to manage sessions, spawn agents, orchestrate team tasks, and perform code reviews, memory searches, and cron scheduling via natural language.

GTA V Browser MCP Server

Enables browsing and extracting files from Grand Theft Auto V's RPF archives, supporting RPF7 format with AES encryption and nested archives.

NOUZ MCP Server

MCP Server for local knowledge management. Semantic + keywords + tags

Outpost

Social media API and MCP server for AI agents that enables publishing to X, Instagram, LinkedIn, Reddit, Bluesky, and Threads from a single endpoint.

mcp-server-toolkit

Production-ready MCP server starter with authentication, observability, and a plugin system for building and deploying MCP servers quickly.

mcp-html2pdfconverter

An MCP server for HTML2PDF Converter. Allows AI agents to seamlessly convert raw HTML strings or live web URLs into high-fidelity PDF documents and save them locally.

kernel-mcp

An MCP server that enables AI-powered Linux kernel development, exposing tools for symbol search, static analysis, build automation, QEMU/GDB debugging, and more via IBM Bob.

MCP SSH Server

Enables Claude Code to control remote servers via SSH for automated deployment, testing, and operations, including command execution and file transfer.

Open Mind

Self-hosted personal knowledge base with semantic search, enabling AI agents to capture, search, and manage thoughts using PostgreSQL with pgvector.

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.

WinApp MCP

A Model Context Protocol server that gives AI assistants full control over native Windows applications — launch, inspect, click, type, screenshot, and test any WinUI3, WPF, WinForms, UWP, or Win32 app.

mcp-server-template-xmcp

A template for creating MCP servers with automatic tool discovery, supporting HTTP and STDIO transports.

flux7-mesh

Guardrail sidecar proxy between AI agents and their MCP/REST/CLI tools. Policy engine, human approval gates, time-limited grants, rate limiting, and OTEL tracing. One Go binary, one YAML config, fail-closed by default.

flstudio-mcp-mac

Enables controlling FL Studio on macOS via MCP, including transport, mixer, channel, MIDI export, and Piano Roll note insertion.

Behance MCP Server

A powerful Model Context Protocol (MCP) server for scraping Behance.net. Extract projects, user profiles, images, and job listings from Behance's creative community without any API keys or subscriptions.

caldav-mcp-wrapper

Enables interacting with CalDAV calendars (like iCloud) through natural language, supporting reading and writing events.

mcp-agent-tools

An MCP server that equips AI agents with real-world tools including file operations, read-only MySQL queries, web summarization, safe calculations, and system info. It uses stdio transport and enforces safety guardrails like SELECT-only database access and AST-based math evaluation.