Discover Awesome MCP Servers

Remote MCP Server Authless

A Cloudflare Workers-based Model Context Protocol server without authentication requirements, allowing users to deploy and customize AI tools that can be accessed from Claude Desktop or Cloudflare AI Playground.

Prover MCP

Enables AI assistants to control Succinct Prover Network operations on Sepolia testnet, including running provers, calibrating hardware, staking tokens, and monitoring earnings through natural language commands.

MCP Memory

An MCP server that enables clients like Cursor, Claude, and Windsurf to remember user information and preferences across conversations using vector search technology.

Databricks MCP Server

A Model Context Protocol server that enables AI assistants to interact with Databricks workspaces, allowing them to browse Unity Catalog, query metadata, sample data, and execute SQL queries.

MCP Server with Azure Communication Services Email

Azure Communication Services - Email MCP

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.

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.

Model Context Protocol (MCP) + Spring Boot Integration

新しいMCPサーバーの機能をSpring Bootを使って試しています。

mcp-servers

サーバーレス環境での MCP サーバー

Word Cloud MCP

Enables creation of word cloud visualizations from various document formats (PDF, Word, TXT, MD) with intelligent text extraction, customizable themes, and multiple output formats (SVG, PNG, JPG, WebP).

DX Cluster MCP Server

Enables interaction with Ham Radio DX Cluster networks to read and create spots, analyze band activity, and track propagation trends through Claude Desktop. It features secure Auth0 authentication and supports various cluster types including DXSpider and AR-Cluster.

Chess MCP

A Model Context Protocol server that enables LLM agents and humans to play chess games together with comprehensive game management capabilities including move validation, draw detection, and game state tracking.

Tarot MCP Server

Provides tarot card reading capabilities with a complete 78-card deck, multiple spread layouts (Celtic Cross, Past-Present-Future, etc.), and detailed card interpretations for divination and daily guidance.

Google Search MCP Server

Enables users to perform Google Custom Search queries through the Model Context Protocol. Requires Google API credentials and Custom Search Engine configuration for web search functionality.

ms_salespower_mcp

MCPサーバーを介して、一般的なAIチャット内で使用できる、有用なセールスユースケースを有効にします。

GS Robot MCP Server

A Model Control Protocol plugin for controlling GS cleaning robots, supporting robot listing, status monitoring, navigation commands, task execution, and remote control operations.

XERT Cycling Training

Connect Claude to XERT cycling analytics - access fitness signature (FTP, LTP, HIE), training load, workouts, and activities.

Arcjet - MCP Server

Arcjet Model Context Protocol (MCP) server. Help your AI agents implement bot detection, rate limiting, email validation, attack protection, data redaction.

SAP BusinessObjects BI MCP Server by CData

This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out our free CData MCP Server for SAP BusinessObjects BI (beta): https://www.cdata.com/download/download.aspx?sku=GJZK-V&type=beta

Naver Flight MCP

A Model Context Protocol (MCP) server built with mcp-framework that provides tools for flight-related operations. This appears to be a template or starter project with example tools that can be extended for flight search and booking functionality.

My Coding Buddy MCP Server

A personal AI coding assistant that connects to various development environments and helps automate tasks, provide codebase insights, and improve coding decisions by leveraging the Model Context Protocol.

NexusMind

An MCP server that leverages graph structures to perform sophisticated scientific reasoning through an 8-stage processing pipeline, enabling AI systems to handle complex scientific queries with dynamic confidence scoring.

Xero MCP Server

Xero会計ソフトと連携できるMCPサーバー

GLM-4.5V MCP Server

Enables multimodal AI capabilities through GLM-4.5V API for image processing, visual querying with OCR/QA/detection modes, and file content extraction from various formats including PDFs, documents, and images.

FastMCP Server Generator

カスタムMCPサーバーの作成を支援する専門的なMCPサーバー

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. ```

MCP Firebird

Firebird SQL データベース向けに Anthropic の Model Context Protocol (MCP) を実装したサーバー。Claude などの LLM が自然言語を通じて Firebird データベース内のデータに安全にアクセス、分析、操作することを可能にする。