Discover Awesome MCP Servers

bq_mcp_server

A Python MCP server that retrieves and caches BigQuery metadata (datasets, tables, columns) and enables secure SQL query execution with cost control, file export, and keyword search.

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.

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.

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.

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.

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.

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.

flux7-mesh

Guardrail sidecar proxy between AI agents and their MCP/REST/CLI tools. Policy engine, human approval gates, time-limited grants, rate limiting, and OTEL tracing. One Go binary, one YAML config, fail-closed by default.

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!

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.

mcp-agent-tools

An MCP server that equips AI agents with real-world tools including file operations, read-only MySQL queries, web summarization, safe calculations, and system info. It uses stdio transport and enforces safety guardrails like SELECT-only database access and AST-based math evaluation.

flstudio-mcp-mac

Enables controlling FL Studio on macOS via MCP, including transport, mixer, channel, MIDI export, and Piano Roll note insertion.

caldav-mcp-wrapper

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

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.

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.

Subwatch MCP

Enables reading public Reddit posts and comments on demand, with tools to search, get recent posts, post details, top comments, and server status. Runs on Cloudflare Workers for use with Claude and Open WebUI.

mcp-cli-catalog

An MCP server that publishes CLI tools on your machine for discoverability by LLMs

Score de Crédito

Enables credit score and risk analysis queries for Brazilian individuals (CPF) and companies (CNPJ) via MCP over HTTP, with a single read-only tool and prepaid usage.

applemail-mcp-server

A local MCP server that lets Claude read macOS Mail.app via JXA, with tools for listing accounts/mailboxes, searching and reading messages. Read-only by default, with optional opt-in write tools for compose drafts and Apple Calendar events.

VA-MCP

An MCP server for checking OWASP Top 10 vulnerabilities during API development testing. It analyzes API information and returns security assessment results to help developers identify potential security issues.

MCP-Odoo

A bridge that allows AI agents to access and manipulate Odoo ERP data through a standardized Model Context Protocol interface, supporting partner information, accounting data, financial records reconciliation, and invoice queries.

Google Workspace MCP Server

Enables management of Google Workspace apps (Docs, Sheets, Gmail, Calendar, Drive) from the command line via Gemini CLI.