Discover Awesome MCP Servers

MCP Master Puppeteer

An advanced MCP server for browser automation using Puppeteer, specifically optimized for token efficiency through minimal data returns and progressive enhancement. It enables agents to navigate pages, capture LLM-optimized screenshots, extract structured content, and perform batch interactions.

AI Intervention Agent

Enables real-time user intervention for MCP agents through a web UI, allowing users to review context and provide feedback when AI agents drift from intent, keeping them on track.

GitLab MCP Server

Enables AI assistants to interact with GitLab projects, allowing users to query merge requests, review discussions, view pipeline test results, search by branch, and respond to comments through natural language.

Android MCP Server

Enables control and automation of Android devices using uiautomator2 and ADB. It supports UI interactions, app management, and shell command execution through the Model Context Protocol.

JakartaMigration

A Model Context Protocol (MCP) server that provides AI coding assistants with specialized tools for analyzing and migrating Java applications from Java EE 8 (javax.) to Jakarta EE 9+ (jakarta.).

Next.js MCP Server

A template MCP server built with Next.js using the Vercel MCP Adapter. Provides a framework for deploying MCP servers with custom tools, prompts, and resources on Vercel with SSE transport support.

Oracle Sales MCP Server by CData

This read-only MCP Server allows you to connect to Oracle Sales data from Claude Desktop through CData JDBC Drivers. Free (beta) read/write servers available at https://www.cdata.com/solutions/mcp

Todo MCP Server

A TypeScript-based server that enables AI agents to create, prioritize, and manage ordered task lists for complex projects. It provides tools for task tracking, status filtering, and progress statistics with persistent storage.

Kodit

A Code Indexing MCP Server that connects AI coding assistants to external codebases, providing accurate and up-to-date code snippets to reduce mistakes and hallucinations.

Excel Explorer

An MCP server that allows LLMs to read, analyze, and interact with Excel files through file operations, data discovery, and comprehensive analysis tools.

MCP Hub Database Server

Enables querying and searching the MCP Hub database to discover MCP servers, view server details, find top servers by popularity or recency, and identify top contributors.

TigerData-mcp-server

Run queries and pull information about your TigerData Cloud's PostgreSQL databases

Vault MCP Server

Enables interaction with HashiCorp Vault to read, write, list, and delete secrets through a containerized MCP server with secure token-based authentication.

web-monitor-mcp-safepoint

The mcp server for safepoint web monitor

OpenTargets MCP Server

Unofficial Model Context Protocol server for accessing Open Targets platform data for gene-drug-disease associations research.

Your Money Left The Chat

A Rust + MCP powered financial tracker that knows exactly where your money ghosted you.

MCP Server

A Multi-Agent Conversation Protocol Server that interfaces with the Exa Search API, allowing agents to perform semantic search operations through a standardized protocol.

MemoraМCP

An MCP-powered storage system for AI agents that provides IPFS-secured, verifiable, and sovereign data storage capabilities.

YouTube to LinkedIn MCP Server

Espejo de

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.

img-gen

Provides tools for generating optimized images via Google's Gemini model and fetching weather forecasts and alerts from the National Weather Service. It enables users to create visual content and retrieve environmental data seamlessly within MCP-compatible clients.

Quack MCP Server

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

Agent Collaboration MCP Server

Enables AI agents to orchestrate a team of sub-agents through tmux sessions for complex task delegation and parallel implementation. It provides tools for launching agents, monitoring their real-time status, and managing communication between them.

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.

Awesome MCP Servers

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

Bureau of Economic Analysis (BEA) MCP Server

Provides access to comprehensive U.S. economic data including GDP, personal income, and regional statistics via the Bureau of Economic Analysis API. It enables users to query datasets and retrieve specific economic indicators for states, counties, and industries through natural language.

Weather MCP Server

Provides real-time weather information for 12 major Chinese cities and global locations using the wttr.in API. Built with the HelloAgents framework, it requires no API keys and supports queries in both Chinese and English.

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.

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.