Discover Awesome MCP Servers

MCP Server on Cloudflare Workers & Azure Functions

A deployable MCP server for Cloudflare Workers or Azure Functions that provides example tools (time, echo, math), prompt templates for code assistance, and configuration resources. Enables AI assistants to interact with edge-deployed services through the Model Context Protocol.

Bilibili MCP Server

Enables interaction with Bilibili (B站) platform through API and web scraping. Supports video search, article search, video info retrieval, comment fetching, danmaku extraction, and article content access.

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.

Concordium MCP Server

Concordium mcp-sever for interacting with the concordium chain

MCP Document Server

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

Wonderland Editor MCP Plugin

Plugin del servidor MCP para el editor de Wonderland

SuperCollider MCP Server

Enables AI assistants to generate and control real-time audio synthesis through natural language descriptions using SuperCollider. Features 10 built-in synth types, pattern sequencing, audio recording, and server lifecycle management for creating sounds from simple English descriptions.

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!

MCP Firebird

Un servidor que implementa el Protocolo de Contexto de Modelos (MCP) de Anthropic para bases de datos Firebird SQL, permitiendo a Claude y otros LLMs acceder, analizar y manipular de forma segura datos en bases de datos Firebird a través del lenguaje natural.

Google Calendar MCP Server by CData

Google Calendar MCP Server by CData

PostgreSQL MCP Server

Enables secure querying of PostgreSQL databases through MCP-compatible clients. Supports read-only SQL execution, table exploration, and connection management with built-in security validation.

😎 Contributing

🔥🔒 Awesome MCP (Model Context Protocol) Security 🖥️

MCP DateTime Server

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

Voice Call MCP Server

Un servidor de Protocolo de Contexto de Modelo que permite a asistentes de IA como Claude iniciar y gestionar llamadas de voz en tiempo real utilizando Twilio y los modelos de voz de OpenAI.

MCP Server with Azure Communication Services Email

Azure Communication Services - Correo electrónico MCP

termiAgent

termiAgent es un asistente de línea de comandos impulsado por LLM que proporciona configuraciones de roles de complementos para crear flujos de trabajo para diferentes tareas. Al mismo tiempo, es un mcp-client que puede conectarse libremente a sus mcp-servers.

Memory-IA MCP Server

Enables AI agents with persistent memory using SQLite and local LLM models through Ollama integration. Provides chat with context retention and multi-client support across VS Code, Gemini-CLI, and terminal interfaces.

BANANA-MCP

An All-in-One Model Context Protocol Server Package that integrates 14 MCP servers (including YouTube, GitHub, Figma, databases) into a single Docker container for use with Claude.

Model Context Protocol (MCP) + Spring Boot Integration

Probando la nueva función del servidor MCP usando Spring Boot.

Doctah-MCP

Enables AI assistants to search and access Arknights game data including operator information, enemy intelligence, skills, talents, and attributes through PRTS.wiki integration. Provides fuzzy search functionality for operators and enemies with clean Markdown output.

🚀 Wayland MCP Server

Servidor MCP para Wayland

gbox

Gru-sandbox (gbox) es un proyecto de código abierto que proporciona un entorno de pruebas autoalojable para la integración de MCP u otros casos de uso de agentes de IA.

BlenderMCP

Connects Claude AI to Blender through the Model Context Protocol, enabling AI-assisted 3D modeling, scene creation, material control, and object manipulation. Supports integration with Poly Haven assets and Hyper3D for AI-generated models.

mcp-server-docker

mcp-server-docker

Swift Test MCP Server

Enables running Swift package tests through the swift test command in specified directories. Provides a secure way for MCP clients to execute Swift tests without requiring full shell access.

mcp-bitbucket

Access all major Bitbucket Cloud features—repositories, pull requests, issues, branches, pipelines, deployments, and more—using a modern Rust codebase. Expose Bitbucket as Model Context Protocol (MCP) tools, ideal for bots, CI/CD, and workflow automation.

Anomaly Detection MCP Server

A server that enables LLMs to detect anomalies in sensor data by providing tools for data retrieval, analysis, visualization, and corrective action execution.