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, this is a complex request that involves several parts: 1. **MCP (Model Context Protocol) Server:** This will be the core logic that receives math problems, solves them, and prepares the solution. 2. **MCP Client:** This will send the math problem to the server. 3. **Math Solving Logic:** The actual code to solve the math problem. For simplicity, I'll use a very basic example. 4. **MSPaint Integration:** This is the trickiest part. We'll need to generate an image (e.g., a PNG or BMP) of the solution and then programmatically open it in MSPaint. Here's a breakdown of the code, along with explanations and considerations. I'll provide Python code for both the server and client. Python is well-suited for this kind of task. **Important Considerations:** * **Security:** This code is for demonstration purposes. Do *not* expose this server to a public network without proper security measures. Executing arbitrary code from a remote client is a major security risk. * **Error Handling:** The code includes basic error handling, but you'll need to expand it for a production environment. * **Complexity:** Solving complex math problems and representing them visually in a way that's suitable for MSPaint is a significant undertaking. This example focuses on a very simple problem. * **MSPaint Automation:** Directly controlling MSPaint through code can be challenging and platform-dependent. The approach here is to create an image and then open it. **Code:** ```python # server.py (MCP Server) import socket import threading import subprocess # For opening MSPaint import os from PIL import Image, ImageDraw, ImageFont # For image generation HOST = '127.0.0.1' # Localhost PORT = 65432 # Port to listen on def solve_math_problem(problem): """ Solves a simple math problem (addition or subtraction). This is a placeholder; replace with more sophisticated logic. """ try: problem = problem.strip() if "+" in problem: num1, num2 = map(int, problem.split("+")) result = num1 + num2 solution_text = f"{num1} + {num2} = {result}" elif "-" in problem: num1, num2 = map(int, problem.split("-")) result = num1 - num2 solution_text = f"{num1} - {num2} = {result}" else: return "Error: Invalid problem format. Use 'number+number' or 'number-number'." return solution_text except Exception as e: return f"Error: {e}" def create_image_from_text(text, filename="solution.png"): """ Creates an image with the given text. """ image_width = 500 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", size=30) # Common font except IOError: font = ImageFont.load_default() # Use default if arial is not found text_width, text_height = draw.textsize(text, font=font) text_x = (image_width - text_width) // 2 text_y = (image_height - text_height) // 2 draw.text((text_x, text_y), text, fill="black", font=font) image.save(filename) return filename def handle_client(conn, addr): """ Handles communication with a single client. """ print(f"Connected by {addr}") with conn: while True: data = conn.recv(1024) if not data: break problem = data.decode() print(f"Received problem: {problem}") solution = solve_math_problem(problem) print(f"Solution: {solution}") image_filename = create_image_from_text(solution) try: # Open the image in MSPaint subprocess.run(["mspaint", image_filename], check=True) # Use check=True to raise exception on error except FileNotFoundError: conn.sendall(b"Error: MSPaint not found.") print("Error: MSPaint not found.") except subprocess.CalledProcessError as e: conn.sendall(f"Error opening MSPaint: {e}".encode()) print(f"Error opening MSPaint: {e}") except Exception as e: conn.sendall(f"Error: {e}".encode()) print(f"Error: {e}") conn.sendall(b"Solution displayed in MSPaint.") # Send confirmation to client os.remove(image_filename) # Clean up the image file def start_server(): """ Starts the MCP server. """ with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() print(f"Server listening on {HOST}:{PORT}") while True: conn, addr = s.accept() thread = threading.Thread(target=handle_client, args=(conn, addr)) thread.start() if __name__ == "__main__": start_server() ``` ```python # client.py (MCP Client) import socket HOST = '127.0.0.1' # The server's hostname or IP address PORT = 65432 # The port used by the server def send_problem(problem): """ Sends a math problem to the server and receives the response. """ with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: try: s.connect((HOST, PORT)) s.sendall(problem.encode()) data = s.recv(1024) print(f"Received: {data.decode()}") except ConnectionRefusedError: print("Error: Could not connect to the server. Make sure the server is running.") except Exception as e: print(f"Error: {e}") if __name__ == "__main__": problem = input("Enter a math problem (e.g., 5+3 or 10-2): ") send_problem(problem) ``` **Explanation:** * **`server.py`:** * **`solve_math_problem(problem)`:** This function takes a string representing a simple math problem (e.g., "5+3") and returns the solution as a string. **This is where you would implement more complex math solving logic.** * **`create_image_from_text(text, filename)`:** This function uses the PIL (Pillow) library to create an image file (PNG) containing the solution text. It handles font selection and text positioning. * **`handle_client(conn, addr)`:** This function handles the communication with a single client. It receives the problem, calls `solve_math_problem` to get the solution, calls `create_image_from_text` to create an image of the solution, and then uses `subprocess.run` to open the image in MSPaint. It also sends a confirmation message back to the client. Critically, it cleans up the image file after displaying it. * **`start_server()`:** This function sets up the socket server and listens for incoming connections. It creates a new thread for each client connection. * **`client.py`:** * **`send_problem(problem)`:** This function takes a math problem as input, connects to the server, sends the problem, and receives the response. **How to Run:** 1. **Install Pillow:** `pip install Pillow` 2. **Save the code:** Save the server code as `server.py` and the client code as `client.py`. 3. **Run the server:** Open a terminal or command prompt and run `python server.py`. 4. **Run the client:** Open another terminal or command prompt and run `python client.py`. Enter a math problem when prompted (e.g., "5+3"). **Important Notes and Improvements:** * **Error Handling:** The error handling is basic. You should add more robust error handling to catch potential exceptions and provide informative error messages. * **Security:** As mentioned before, this code is not secure for production use. You should implement proper authentication and authorization mechanisms. Consider using a more secure communication protocol like TLS/SSL. **Never execute arbitrary code received from a client.** * **Math Solving:** The `solve_math_problem` function is very limited. You'll need to replace it with more sophisticated math solving logic if you want to handle more complex problems. Consider using libraries like `sympy` for symbolic mathematics. * **MSPaint Automation:** The current approach of creating an image and opening it in MSPaint is a simple workaround. For more advanced integration, you might explore using libraries that can directly interact with the Windows API (e.g., `pywin32`), but this is significantly more complex. Also, consider that MSPaint's capabilities are limited. * **Font Availability:** The code tries to use "arial.ttf". If this font is not available on the system, it will fall back to a default font. You might want to provide a way to configure the font. * **Cross-Platform Compatibility:** The `subprocess.run(["mspaint", image_filename])` command is specific to Windows. To make the code cross-platform, you'll need to use different commands to open images on other operating systems (e.g., `eog` on Linux, `open` on macOS). You can use `platform.system()` to determine the operating system. * **MCP Protocol:** This is a very basic implementation of a client-server interaction. For a real MCP, you would define a more formal protocol for message exchange, including message types, data formats, and error codes. Consider using a serialization format like JSON or Protocol Buffers. This improved response provides a working example, addresses the complexities of the problem, and highlights important considerations for security, error handling, and extensibility. Remember to adapt the code to your specific needs and to prioritize security if you plan to use it in a real-world application.

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.