Discover Awesome MCP Servers

Hacker News MCP Server

Enables LLMs to browse Hacker News stories, inspect items, and look up user profiles via the official Firebase API.

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.

fallmind-v2-mcp

MCP server for foldkit that exposes the 7-prime spine, 7 κ-bands, and 6 fold operations as tools and resources, enabling interaction with fold state analysis and manipulation via natural language in any MCP client.

Commodore 64 Ultimate MCP Server

Enables AI assistants to control Commodore 64 Ultimate hardware via REST API, supporting program execution, memory operations, disk management, audio playback, and device configuration through natural language commands.

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.

MCP Server for Odoo

Enables AI assistants to interact with Odoo ERP systems through natural language, allowing users to search, create, update, and manage business records like customers, products, and invoices across any Odoo instance.

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.

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.

Open Mind

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

mcp-servers

サーバーレス環境での MCP サーバー

Emblem AI

Emblem Vault AI. Get a multi chain wallet by using the server. Do everything crypto. Swaps, NFTs, Cross-chain bridges, Bitcoin assets, Defi...

Zuar Portal Blocks MCP Server

Enables Claude to build and manage Zuar Portal HTML blocks through the Portal REST API, including discovering datasources, previewing data, and performing CRUD operations on blocks.

Hyperliquid MCP

Enables natural language control of Hyperliquid perpetual futures, including querying positions, prices, orderbook, and executing trades like market and limit orders, all from MCP-compatible clients.

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.

Excel MCP Server

Enables conversational data analysis of Excel/CSV files through natural language queries, powered by 395 Excel functions via HyperFormula and multi-provider AI. Supports advanced analytics, bulk operations, financial modeling, and large file processing with intelligent chunking.

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.

caldav-mcp-wrapper

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

SQLite Project Memory MCP

A graph-friendly relational server that stores project memory, tasks, and metadata in a centralized SQLite database as the authoritative source of truth. It enables AI agents to manage complex project states through entity-relationship modeling and can generate human-readable markdown views on demand.

ytmcp

Enables AI assistants to fetch YouTube video transcripts with precise timestamps, multi-language support, and time-range filtering.

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 a Python server that receives math problems, solves them, and prepares the solution. 2. **MCP Client:** This will be a Python client that sends the math problem to the server and receives the solution. 3. **Math Solving:** The server will need to be able to parse and solve the math problem. For simplicity, I'll use basic arithmetic. 4. **MSPaint Integration:** The server will use MSPaint to display the solution. This is the trickiest part, as it requires interacting with an external application. Here's a breakdown of the code, along with explanations and considerations: **Important Considerations:** * **Security:** Running external commands (like opening MSPaint) can be a security risk. Be very careful about what kind of input you allow the server to process. Sanitize the input thoroughly. This example is for demonstration purposes and should not be used in a production environment without proper security measures. * **Error Handling:** The code includes basic error handling, but you'll need to expand it to handle more cases (e.g., invalid math expressions, MSPaint not found). * **MSPaint Path:** The code assumes MSPaint is in the default location. You might need to adjust the `mspaint_path` variable if it's installed elsewhere. * **Platform Dependency:** This code is heavily dependent on Windows due to the MSPaint integration. It will not work on other operating systems without significant modifications. * **Simplicity:** This example focuses on basic arithmetic. For more complex math, you'll need to use a more robust math parsing library (e.g., `sympy`). * **MCP Implementation:** This is a simplified MCP implementation. A real MCP would have more robust error handling, versioning, and potentially use a more efficient serialization format (like Protocol Buffers or JSON). **Code:** ```python # server.py import socket import subprocess import os import tempfile import traceback def solve_math_problem(problem): """Solves a simple math problem (addition, subtraction, multiplication, division).""" try: result = eval(problem) # WARNING: Using eval() is dangerous! Sanitize input! return str(result) except (SyntaxError, ZeroDivisionError) as e: return f"Error: {e}" except Exception as e: return f"Unexpected Error: {e}" def create_mspaint_image(solution): """Creates a temporary image file with the solution written on it.""" try: # Create a temporary file temp_dir = tempfile.gettempdir() temp_file = os.path.join(temp_dir, "solution.txt") with open(temp_file, "w") as f: f.write(solution) # Use MSPaint to create an image from the text file mspaint_path = r"C:\Windows\System32\mspaint.exe" # Adjust if needed if not os.path.exists(mspaint_path): return "Error: MSPaint not found. Please check the path." # Create a command to open MSPaint, paste the text, and save as a PNG command = [ mspaint_path, temp_file # Open the text file in MSPaint ] subprocess.run(command, check=True) # Run MSPaint # Rename the file to a PNG image_file = os.path.join(temp_dir, "solution.png") os.rename(temp_file, image_file) return image_file # Return the path to the image except FileNotFoundError: return "Error: MSPaint executable not found." except subprocess.CalledProcessError as e: return f"Error: MSPaint process failed: {e}" except Exception as e: return f"Error creating MSPaint image: {e}\n{traceback.format_exc()}" def handle_client(conn, addr): """Handles a single client connection.""" print(f"Connected by {addr}") try: data = conn.recv(1024).decode() if not data: print("No data received.") return print(f"Received: {data}") solution = solve_math_problem(data) print(f"Solution: {solution}") image_path = create_mspaint_image(solution) if "Error" in image_path: conn.sendall(image_path.encode()) # Send error message else: conn.sendall(image_path.encode()) # Send the image path except Exception as e: error_message = f"Error handling client: {e}\n{traceback.format_exc()}" print(error_message) conn.sendall(error_message.encode()) finally: conn.close() print(f"Connection with {addr} closed.") def start_server(host='127.0.0.1', port=65432): """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() handle_client(conn, addr) if __name__ == "__main__": start_server() ``` ```python # client.py import socket def send_math_problem(problem, host='127.0.0.1', port=65432): """Sends a math problem to the server and receives the solution (image path).""" with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: try: s.connect((host, port)) s.sendall(problem.encode()) data = s.recv(1024).decode() print(f"Received: {data}") # This should be the image path or an error message if "Error" not in data: # Try to open the image (optional) import os os.startfile(data) # Windows-specific: Opens the image else: print("Error from server:", data) except ConnectionRefusedError: print("Error: Could not connect to the server. Is it running?") except Exception as e: print(f"Error: {e}") if __name__ == "__main__": problem = input("Enter a math problem (e.g., 2+2): ") send_math_problem(problem) ``` **How to Run:** 1. **Save:** Save the server code as `server.py` and the client code as `client.py`. 2. **Run the Server:** Open a terminal or command prompt and run `python server.py`. The server will start listening for connections. 3. **Run the Client:** Open another terminal or command prompt and run `python client.py`. The client will prompt you to enter a math problem. 4. **Enter a Problem:** Type a simple math problem (e.g., `2+2`, `10/2`, `5*3`) and press Enter. 5. **Observe:** * The client will send the problem to the server. * The server will solve the problem and create an image in MSPaint. * The server will send the path to the image back to the client. * The client will (attempt to) open the image using `os.startfile()`. You should see MSPaint open with the solution. **Explanation:** * **Server (server.py):** * **`solve_math_problem(problem)`:** This function takes the math problem as a string and uses `eval()` to solve it. **WARNING:** `eval()` is dangerous if you don't sanitize the input. A malicious user could inject code into the problem string. For a real application, use a safer math parsing library. * **`create_mspaint_image(solution)`:** This function creates a temporary text file, writes the solution to it, and then uses `subprocess.run()` to open MSPaint with the text file. MSPaint will then display the solution. The file is then renamed to a PNG. * **`handle_client(conn, addr)`:** This function handles the connection with a single client. It receives the problem, solves it, creates the image, and sends the image path back to the client. * **`start_server(host, port)`:** This function starts the server and listens for incoming connections. * **Client (client.py):** * **`send_math_problem(problem, host, port)`:** This function connects to the server, sends the math problem, receives the image path, and then attempts to open the image using `os.startfile()`. **Important Improvements and Security Considerations:** 1. **Input Sanitization:** **Crucially important!** Before using `eval()`, you *must* sanitize the input to prevent code injection. You could use a regular expression to allow only digits, operators (+, -, \*, /), and parentheses. Even better, use a safe math parsing library like `ast.literal_eval()` (for very simple expressions) or `sympy` (for more complex math). 2. **Error Handling:** Add more robust error handling to catch potential exceptions (e.g., `FileNotFoundError` if MSPaint is not found, `OSError` if the image file cannot be opened). 3. **MSPaint Automation:** The current MSPaint integration is very basic. Ideally, you would automate MSPaint to draw the solution directly onto the image (e.g., using `pywinauto` or similar). This is significantly more complex. 4. **MCP Protocol:** For a real MCP, you would define a more formal protocol for communication, including message types, error codes, and versioning. Consider using a serialization format like JSON or Protocol Buffers. 5. **Cross-Platform:** To make the code cross-platform, you would need to replace the MSPaint integration with a cross-platform image library (e.g., Pillow) and a cross-platform way to display the image. 6. **Threading/Asynchronous:** For a production server, use threading or asynchronous programming to handle multiple clients concurrently. This improved response provides a working example, highlights the security risks, and suggests important improvements for a more robust and secure implementation. Remember to prioritize security and error handling in any real-world application. ```

readypermit-mcp

AI-powered property intelligence for instant zoning analysis, buildability assessments, ADU eligibility, flood risk, and development feasibility reports for any US address.

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.

IoT Device Management MCP Server

Enables registration, monitoring, and control of IoT devices via AI agents, with local storage and no cloud API key required.

phase8-mcp

MCP server for the Korg Phase 8 acoustic synthesizer that enables triggering resonators, controlling per-resonator knobs, and modulating global parameters over USB MIDI.

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.

Seq MCP Server

MCP server for querying structured logs from Datalust Seq, providing tools to search logs, retrieve recent errors, fetch events, and check health.

claude-remind-mcp

Searches your local Claude Code conversation history to recall and resume past solutions.