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

MCP Server Plugin for Wonderland Editor

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 inter-process communication (MCP), mathematical problem solving, and integration with MSPaint. Here's a conceptual outline and a simplified Python example to illustrate the core ideas. Keep in mind that a fully robust solution would require significantly more code and error handling. **Conceptual Outline** 1. **MCP Server (Python):** * Listens for incoming connections on a specific port. * Receives a mathematical problem (as a string) from the client. * Parses the problem. * Solves the problem. * Generates a solution string (including steps). * Sends the solution string back to the client. 2. **MCP Client (Python):** * Connects to the MCP server. * Prompts the user to enter a math problem. * Sends the problem to the server. * Receives the solution from the server. * Creates a temporary image file (e.g., using PIL/Pillow). * Draws the solution text onto the image. * Saves the image. * Opens the image in MSPaint using `os.system` or `subprocess`. **Simplified Python Example (Illustrative)** ```python # server.py import socket import threading import ast import traceback HOST = '127.0.0.1' # Standard loopback interface address (localhost) PORT = 65432 # Port to listen on (non-privileged ports are > 1023) def solve_problem(problem): """ A very basic problem solver. Expand this significantly! """ try: # WARNING: Using eval() is DANGEROUS with untrusted input. # This is ONLY for demonstration. Use a proper math parser. result = eval(problem) solution = f"Problem: {problem}\nSolution: {result}" return solution except Exception as e: return f"Error solving problem: {e}\n{traceback.format_exc()}" 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}") solution = solve_problem(problem) conn.sendall(solution.encode()) print(f"Sent solution") def server_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__": server_main() ``` ```python # client.py import socket import os import subprocess from PIL import Image, ImageDraw, ImageFont HOST = '127.0.0.1' # The server's hostname or IP address PORT = 65432 # The port used by the server IMAGE_FILE = "solution.png" # Name of the image file def create_image(text, filename): """Creates an image with the given text.""" img = Image.new('RGB', (800, 600), color='white') # Adjust size as needed d = ImageDraw.Draw(img) font = ImageFont.truetype("arial.ttf", 20) # Or another font you have d.text((10, 10), text, fill='black', font=font) img.save(filename) def client_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) # Increase buffer size if needed solution = data.decode() print(f"Received solution:\n{solution}") create_image(solution, IMAGE_FILE) # Open MSPaint (Windows-specific) try: #os.system(f"mspaint {IMAGE_FILE}") # simpler but less robust subprocess.run(["mspaint", IMAGE_FILE]) # more robust except FileNotFoundError: print("MSPaint not found. Make sure it's in your PATH.") except Exception as e: print(f"Error opening MSPaint: {e}") if __name__ == "__main__": client_main() ``` **Key Improvements and Explanations** * **Error Handling:** Includes `try...except` blocks to catch potential errors during problem solving, image creation, and MSPaint execution. The server also includes a traceback to help debug server-side errors. * **Image Creation (PIL/Pillow):** Uses the Pillow library to create an image and draw the solution text onto it. You'll need to install Pillow: `pip install Pillow`. You'll also need to specify a font file that exists on your system (e.g., "arial.ttf"). * **MSPaint Integration:** Uses `subprocess.run(["mspaint", IMAGE_FILE])` to open the image in MSPaint. This is generally more robust than `os.system`. It also includes a check to see if MSPaint is found. * **Encoding/Decoding:** Explicitly encodes and decodes strings when sending data over the socket. * **Threading (Server):** The server now uses threads to handle multiple client connections concurrently. * **`solve_problem` function:** This is now a function, making the code more organized. **IMPORTANT:** The `eval()` function is extremely dangerous with untrusted input. See the warnings below. * **Buffer Size:** Increased the receive buffer size on the client to 4096 bytes. Adjust as needed based on the expected size of the solution string. **How to Run** 1. **Save:** Save the code as `server.py` and `client.py`. 2. **Install Pillow:** `pip install Pillow` 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 to enter a math problem (e.g., `2 + 2`). 6. **MSPaint:** The client will create an image with the solution and attempt to open it in MSPaint. **Important Considerations and Next Steps** * **Security (VERY IMPORTANT):** **DO NOT USE `eval()` IN PRODUCTION CODE!** It is extremely vulnerable to code injection if the input is not carefully sanitized. Use a safe math parsing library like `ast.literal_eval()` (for very simple expressions) or a more robust library like `sympy`. `ast.literal_eval()` only supports basic Python literals (strings, numbers, tuples, lists, dicts, booleans, `None`). `sympy` is a full-featured symbolic mathematics library. ```python # Example using ast.literal_eval (SAFER for simple expressions) import ast def solve_problem_safe(problem): try: result = ast.literal_eval(problem) # Safer than eval() solution = f"Problem: {problem}\nSolution: {result}" return solution except (ValueError, SyntaxError) as e: return f"Error: Invalid expression: {e}" # Example using sympy (for more complex math) # import sympy # from sympy.parsing.mathematica import parse_mathematica # if you want to parse mathematica syntax # def solve_problem_sympy(problem): # try: # #parsed_expr = sympy.parsing.mathematica.parse_mathematica(problem) # if using mathematica syntax # parsed_expr = sympy.sympify(problem) # sympy's default parser # result = sympy.simplify(parsed_expr) # solution = f"Problem: {problem}\nSolution: {result}" # return solution # except Exception as e: # return f"Error: {e}" ``` * **Error Handling:** Add more comprehensive error handling to both the client and server. Handle socket errors, file I/O errors, and MSPaint errors gracefully. * **Problem Parsing:** Implement a more sophisticated problem parser. Consider using a library like `sympy` to handle a wider range of mathematical expressions. * **Solution Formatting:** Improve the formatting of the solution text in the image. Use different fonts, colors, and layout techniques to make it more readable. * **User Interface:** Consider using a GUI library like Tkinter, PyQt, or Kivy to create a more user-friendly interface for the client. * **MCP Protocol:** Define a more formal MCP protocol for communication between the client and server. This could involve defining message types, error codes, and data formats. Consider using JSON or Protocol Buffers for serialization. * **Platform Independence:** The MSPaint integration is Windows-specific. To make the client platform-independent, you'll need to use a different image viewer or editor that is available on other operating systems. You could also allow the user to specify the image viewer to use. * **Security (Again):** If this is going to be used in any kind of networked environment, think very carefully about security. Authentication, authorization, and encryption may be necessary. This expanded example provides a much more solid foundation for building your MCP math problem solver. Remember to prioritize security and error handling as you add more features. Good luck!

MCP Firebird

Um servidor que implementa o Protocolo de Contexto de Modelo (MCP) da Anthropic para bancos de dados Firebird SQL, permitindo que Claude e outros LLMs acessem, analisem e manipulem dados em bancos de dados Firebird de forma segura através de linguagem 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

Um servidor de Protocolo de Contexto de Modelo que permite que assistentes de IA como o Claude iniciem e gerenciem chamadas de voz em tempo real usando o Twilio e os modelos de voz da OpenAI.

MCP Server with Azure Communication Services Email

Azure Communication Services - Email MCP (MCP pode se referir a "Managed Communication Provider" ou outro termo específico dependendo do contexto. Se for o caso, forneça mais contexto para uma tradução mais precisa.)

termiAgent

termiAgent é um assistente de linha de comando impulsionado por LLM que fornece configurações de função de plug-in para criar fluxos de trabalho para diferentes tarefas. Ao mesmo tempo, é um mcp-client que pode se conectar livremente aos seus 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

Testando um novo recurso do 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) é um projeto de código aberto que fornece um sandbox auto-hospedável para integração com MCP ou outros 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.