Discover Awesome MCP Servers

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.

Open Mind

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

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.

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.

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.

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.

nj-realtor-mcp

A production-grade MCP server enabling Claude to perform comprehensive NJ real estate workflows including property search, valuation, neighborhood intelligence, investment analysis, and agent tools via 20 tools and 15+ data sources.

pdf-inspector

Classifies PDFs (text-based vs scanned vs image vs mixed), extracts text, and converts to clean Markdown over Streamable HTTP MCP. Supports remote PDFs via URL or base64, with optional page restriction and OCR fallback detection.

solana-infra-mcp

Provides Solana infrastructure awareness including RPC health, priority fee estimation, leader schedule, chain state, latency comparison, and keyless transaction submission.

Respira for WordPress

MCP server for AI-assisted WordPress editing across 12 page builders. 172 tools for content management, page builder editing, WooCommerce, SEO analysis, accessibility scanning, and site intelligence. Edits native builder formats (Elementor, Bricks, Divi, Gutenberg, Beaver Builder, and 7 more) with duplicate-before-edit safety, optimistic locking, and surgical element-level operations

COMSOL MCP Server

Enables AI agents to automate COMSOL Multiphysics simulations, including model management, geometry building, physics configuration, meshing, solving, and results visualization through the MCP protocol.

message-container

Enables AI clients to read and search macOS Messages history through a read-only MCP interface.

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.

LM_PS_MCP

A minimal MCP server that exposes a persistent PowerShell session to LM Studio, enabling command execution, directory navigation, and environment variable management via tools like ps_run, cd, and env_set.

MCP Client-Server Sandbox for LLM Augmentation

Banco de pruebas completo para aumentar la inferencia de LLM (local o en la nube) con MCP Cliente-Servidor. Entorno de pruebas de baja fricción para la validación del servidor MCP y la evaluación agentica.

Quack MCP Server

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

Awesome MCP Servers

Una colección exhaustiva de servidores de Protocolo de Contexto de Modelo (MCP).

self-hosted-pages-mcp

Enables AI assistants to deploy and manage static websites on EdgeOne Pages through a self-hosted MCP server. Supports one-click deployment, custom domain binding, and direct API access.

Email Sender MCP Server

Enables sending emails through SMTP with support for multiple recipients, attachments, CC/BCC, and both plain text and HTML formats. Includes preset configurations for common email providers like Gmail, QQ, Outlook, and 163.

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.

Model Context Protocol (MCP) MSPaint App Automation

Okay, this is a more complex request involving several parts: a server, a client, math problem solving, and integration with MSPaint. I'll provide a conceptual outline and Python code snippets to get you started. Keep in mind that this is a simplified example, and a production-ready solution would require more robust error handling, security, and potentially a more sophisticated drawing mechanism. **Conceptual Outline** 1. **Server (Python):** * Listens for client connections. * Receives math problems (as strings). * Solves the problem (using `eval` or a safer alternative like `ast.literal_eval` for simple expressions, or a dedicated math library for more complex problems). * Generates a solution string (including steps, if possible). * Sends the solution string back to the client. 2. **Client (Python):** * Connects to the server. * Prompts the user for a math problem. * Sends the problem to the server. * Receives the solution from the server. * Generates a simple image of the solution using a library like Pillow (PIL). * Opens the image in MSPaint using `os.system` or `subprocess`. **Code Snippets (Python)** **Server (server.py):** ```python import socket import threading import ast # Safer alternative to eval for simple expressions HOST = '127.0.0.1' # Standard loopback interface address (localhost) PORT = 65432 # Port to listen on (non-privileged ports are > 1023) def handle_client(conn, addr): 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}") try: # Safely evaluate the expression (use ast.literal_eval for simple expressions) solution = str(ast.literal_eval(problem)) # VERY IMPORTANT: See security notes below solution_string = f"Problem: {problem}\nSolution: {solution}" except (SyntaxError, NameError, TypeError) as e: solution_string = f"Error: Invalid problem format or unsupported operation: {e}" except Exception as e: solution_string = f"Error: An unexpected error occurred: {e}" conn.sendall(solution_string.encode()) def main(): 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__": main() ``` **Client (client.py):** ```python import socket import os import subprocess from PIL import Image, ImageDraw, ImageFont # Install Pillow: pip install Pillow HOST = '127.0.0.1' # The server's hostname or IP address PORT = 65432 # The port used by the server def create_image(text, filename="solution.png"): """Creates a simple image with the given text.""" image_width = 800 image_height = 600 img = Image.new('RGB', (image_width, image_height), color='white') d = ImageDraw.Draw(img) try: font = ImageFont.truetype("arial.ttf", size=24) # You might need to adjust the font path except IOError: font = ImageFont.load_default() # Use a default font if Arial is not found d.text((50, 50), text, fill='black', font=font) img.save(filename) return filename def open_mspaint(image_path): """Opens the image in MSPaint.""" try: # Use subprocess for better control and error handling subprocess.run(["mspaint", image_path], check=True) except FileNotFoundError: print("MSPaint not found. Make sure it's in your system's PATH.") except subprocess.CalledProcessError as e: print(f"Error opening MSPaint: {e}") def main(): with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.connect((HOST, PORT)) problem = input("Enter a math problem: ") s.sendall(problem.encode()) data = s.recv(4096) # Increased buffer size solution = data.decode() print(f"Received solution: {solution}") image_file = create_image(solution) open_mspaint(image_file) if __name__ == "__main__": main() ``` **How to Run:** 1. **Install Pillow:** `pip install Pillow` 2. **Save:** Save the server code as `server.py` and the client code as `client.py`. 3. **Run the Server:** Open a terminal and run `python server.py`. 4. **Run the Client:** Open another terminal and run `python client.py`. 5. **Enter a Problem:** The client will prompt you for a math problem. Enter something like `2 + 2` or `3 * 5`. 6. **MSPaint Opens:** MSPaint should open with an image containing the problem and the solution. **Important Considerations and Improvements:** * **Security (VERY IMPORTANT):** The use of `eval` (or even `ast.literal_eval` with user-provided input) is *extremely dangerous* in a production environment. It allows arbitrary code execution if the input is not carefully sanitized. **Never use `eval` or `ast.literal_eval` with untrusted input.** Instead, use a dedicated math parsing library like `sympy` or implement a safe expression evaluator. For example, you could create a function that only allows specific operators and numbers. The example above uses `ast.literal_eval` which is safer than `eval` but still has risks if the input is not carefully controlled. It's suitable for *very* simple expressions. * **Error Handling:** The code includes basic error handling, but you should add more robust error handling, especially around network connections and file operations. * **Solution Steps:** Generating detailed solution steps is a complex task. Libraries like `sympy` can help with this, but it requires more advanced programming. * **Drawing in MSPaint:** The current code creates a simple image and opens it in MSPaint. To draw directly in MSPaint, you would need to use the Windows API (using libraries like `pywin32`) to control MSPaint's drawing functions. This is significantly more complex. Consider using a more powerful drawing library like `matplotlib` or `seaborn` if you need more sophisticated graphics. * **Multi-threading:** The server uses threads to handle multiple clients concurrently. This is a good practice for scalability. * **Image Font:** The client code tries to use Arial font. If it's not available, it falls back to a default font. You might need to adjust the font path depending on your system. * **MSPaint Location:** The `open_mspaint` function assumes that MSPaint is in your system's PATH. If it's not, you'll need to provide the full path to `mspaint.exe`. * **Protocol:** This example uses a very simple text-based protocol. For more complex interactions, consider using a more structured protocol like JSON or Protocol Buffers. * **Dependencies:** Make sure you have the necessary libraries installed (`Pillow`). This comprehensive response provides a solid foundation for building your MCP server/client application. Remember to prioritize security and error handling as you develop your project further. Good luck!

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.

OpenTelemetry MCP Server

Enables AI agents to query Prometheus metrics and Loki logs for intelligent alert investigation and troubleshooting. Provides service discovery, metric querying, log searching, and correlation tools to help identify root causes of issues.

sbinfo

MCP server for querying Korean school budget, unit projects, and special plans through the School Alert (학교알리미) open data API.