Discover Awesome MCP Servers

Migadu MCP Server

Enables AI assistants to manage Migadu email hosting services through natural language, including creating mailboxes, setting up aliases, configuring autoresponders, and handling bulk operations efficiently.

Ava MCP Adapter

A REST bridge that exposes Ava Agent's AI capabilities as MCP tools, enabling interaction with CommandDeck specialist brains through chat, project management, memory recall, and skill invocation.

UniFi Network MCP Server

Enables AI assistants to manage UniFi network infrastructure through 50+ tools covering devices, clients, networks, WiFi, firewall rules, and guest access using the official UniFi Network API.

Windows CLI MCP Server

A Model Context Protocol server that provides secure command-line access to Windows systems, allowing MCP clients like Claude Desktop to safely execute commands in PowerShell, CMD, and Git Bash shells with configurable security controls.

Multi MCP

A flexible proxy server that aggregates multiple backend MCP servers into a single interface using STDIO or SSE transports. It supports dynamic server management via an HTTP API and utilizes namespacing to prevent tool conflicts across connected services.

Google Search Console MCP Server

Connects Google Search Console with Claude AI to analyze SEO data through natural language, enabling search analytics reporting, URL inspection, indexing status checks, sitemap management, and data visualization for SEO professionals.

ikaliMCP Server

Provides a secure interface for AI assistants to interact with penetration testing tools like nmap, hydra, sqlmap, and nikto for educational cybersecurity purposes. Includes input sanitization and runs in a Docker container with Kali Linux tools for authorized testing scenarios.

Claude Code AI Collaboration MCP Server

An MCP server that enables multi-provider AI collaboration using models like DeepSeek, OpenAI, and Anthropic through strategies such as parallel execution and consensus building. It provides specialized tools for side-by-side content comparison, quality review, and iterative refinement across different AI providers.

BookStack MCP Server

Enables searching and retrieving content from BookStack knowledge bases via the BookStack API. It provides structured page data with clean HTML-to-text conversion for seamless integration with AI models.

mcp-sse-server-demo

Demo del servidor SSE MCP

BMKG MCP Server

An unofficial MCP server that provides access to Indonesia's BMKG data, including real-time earthquake reports, village-level weather forecasts, and extreme weather alerts. It enables users to search for location codes and retrieve detailed meteorological and geophysical information through natural language.

OpenXAI MCP Server

Provides tools for evaluating and benchmarking AI explanation methods through a standard interface that can be used with AI assistants and MCP-compatible applications.

Asana MCP Server

An MCP (Multi-Agent Conversation Protocol) server that enables interacting with the Asana API through natural language commands for task management, project organization, and team collaboration.

Slack MCP Server

A FastMCP-based server that provides complete Slack integration for Cursor IDE, allowing users to interact with Slack API features using natural language.

Baby-SkyNet

Provides Claude AI with persistent, searchable memory management across sessions using SQL database, semantic analysis with multi-provider LLM support (Anthropic/Ollama), vector search via ChromaDB, and graph-based knowledge relationships through Neo4j integration.

HiveFlow MCP Server

Connects AI assistants (Claude, Cursor, etc.) directly to the HiveFlow automation platform, allowing them to create, manage, and execute automation flows through natural language commands.

Jokes MCP Server

An MCP server that allows Microsoft Copilot Studio to fetch random jokes from three sources: Chuck Norris jokes, Dad jokes, and Yo Mama jokes.

Usher MCP

Enables users to search and view detailed movie information from TMDB, including cast, ratings, and showtimes, through an interactive widget interface.

spotify-mcp

An MCP server that enables users to control Spotify playback, search music, and manage playlists through natural conversation. It is updated for the February 2026 Spotify Web API changes and supports full playlist CRUD operations.

mcp_server

Okay, I can help you outline the steps and provide some code snippets to guide you in implementing a sample MCP (Media Control Protocol) server using a Dolphin MCP client. Keep in mind that this is a simplified example, and a full implementation would require more robust error handling, state management, and feature support. **Conceptual Overview** 1. **Dolphin MCP Client:** This is the application (e.g., a media player, a control panel) that sends MCP commands to the server. We'll assume you have a Dolphin MCP client already available or are using a library that emulates one. 2. **MCP Server:** This is the application you'll build. It listens for incoming MCP connections, parses the commands, performs actions based on those commands, and sends responses back to the client. **Steps to Implement a Sample MCP Server** 1. **Choose a Programming Language and Libraries:** * **Python:** A good choice for rapid prototyping and ease of use. Use the `socket` library for network communication. * **Node.js:** Suitable for asynchronous, event-driven servers. Use the `net` module. * **Java:** A robust option for larger, more complex servers. Use the `java.net` package. * **C#:** Well-suited for Windows environments. Use the `System.Net.Sockets` namespace. For this example, I'll use Python because it's concise and widely accessible. 2. **Set up a Socket Server:** * Create a socket that listens on a specific port (e.g., 9000). * Accept incoming connections from clients. 3. **Receive and Parse MCP Commands:** * Read data from the socket. * Parse the incoming data as MCP commands. You'll need to understand the MCP command format (e.g., command codes, parameters). Refer to the Dolphin MCP documentation for details. * Common MCP commands include: * `PLAY` * `PAUSE` * `STOP` * `SEEK` * `VOLUME` * `STATUS` 4. **Implement Command Handlers:** * Create functions or methods to handle each MCP command. * These handlers will perform the appropriate actions (e.g., start playback, pause playback, set the volume). * For this sample, we'll simulate these actions (e.g., print a message to the console). 5. **Send Responses:** * After processing a command, send a response back to the client. * Responses typically include a status code (e.g., `OK`, `ERROR`) and any relevant data. **Python Example Code** ```python import socket HOST = '127.0.0.1' # Standard loopback interface address (localhost) PORT = 9000 # Port to listen on (non-privileged ports are > 1023) def handle_play(): print("Received PLAY command. Simulating playback...") return "OK: Playing" def handle_pause(): print("Received PAUSE command. Simulating pause...") return "OK: Paused" def handle_stop(): print("Received STOP command. Simulating stop...") return "OK: Stopped" def handle_seek(time): print(f"Received SEEK command. Seeking to {time}...") return f"OK: Seeked to {time}" def handle_volume(level): print(f"Received VOLUME command. Setting volume to {level}...") return f"OK: Volume set to {level}" def handle_status(): print("Received STATUS command. Returning status...") return "OK: Status - Playing" # Replace with actual status def process_command(command): """Parses and executes MCP commands.""" parts = command.split(" ") command_name = parts[0].upper() if command_name == "PLAY": return handle_play() elif command_name == "PAUSE": return handle_pause() elif command_name == "STOP": return handle_stop() elif command_name == "SEEK": if len(parts) > 1: try: time = int(parts[1]) return handle_seek(time) except ValueError: return "ERROR: Invalid time format" else: return "ERROR: Missing time parameter" elif command_name == "VOLUME": if len(parts) > 1: try: level = int(parts[1]) return handle_volume(level) except ValueError: return "ERROR: Invalid volume level" else: return "ERROR: Missing volume level parameter" elif command_name == "STATUS": return handle_status() else: return "ERROR: Unknown command" with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() print(f"Listening on {HOST}:{PORT}") conn, addr = s.accept() with conn: print(f"Connected by {addr}") while True: data = conn.recv(1024) # Receive up to 1024 bytes if not data: break # Client disconnected command = data.decode().strip() # Decode bytes to string and remove leading/trailing whitespace print(f"Received command: {command}") response = process_command(command) conn.sendall(response.encode()) # Encode the response back to bytes print("Server stopped.") ``` **Explanation:** * **`socket.socket()`:** Creates a socket object. * **`s.bind()`:** Binds the socket to a specific address and port. * **`s.listen()`:** Enables the server to accept connections. * **`s.accept()`:** Accepts a connection from a client. Returns a new socket object (`conn`) representing the connection and the client's address (`addr`). * **`conn.recv()`:** Receives data from the client. * **`data.decode()`:** Decodes the received bytes into a string. * **`process_command()`:** Parses the command and calls the appropriate handler function. * **`conn.sendall()`:** Sends data back to the client. * **`data.encode()`:** Encodes the response string back into bytes. **How to Run:** 1. Save the code as a Python file (e.g., `mcp_server.py`). 2. Run the script from your terminal: `python mcp_server.py` **Testing with a Simple Client (netcat)** You can use `netcat` (often abbreviated as `nc`) to simulate a Dolphin MCP client for testing: 1. Open a new terminal window. 2. Connect to the server: `nc localhost 9000` 3. Type MCP commands (e.g., `PLAY`, `PAUSE`, `VOLUME 50`, `STATUS`) and press Enter. 4. You should see the server's responses in the `netcat` terminal and the server's output in the server's terminal. **Important Considerations:** * **Error Handling:** The example code has minimal error handling. You should add more robust error handling to catch exceptions and handle invalid input. * **MCP Specification:** Refer to the official Dolphin MCP documentation for the exact command formats, status codes, and data structures. The example code assumes a simplified command format. * **Threading/Asynchronous Operations:** For a production server, use threading or asynchronous operations (e.g., `asyncio` in Python, `Promises` in Node.js) to handle multiple client connections concurrently. The single-threaded example above will only handle one client at a time. * **Security:** If you're exposing the server to a network, consider security implications (e.g., authentication, authorization). * **State Management:** The server needs to maintain state (e.g., the current playback status, volume level) to respond correctly to commands. * **Real Media Control:** The example only *simulates* media control. To actually control media playback, you'll need to integrate with a media player library or API (e.g., VLC, GStreamer). **Next Steps:** 1. **Study the Dolphin MCP Specification:** This is crucial for understanding the exact command formats and protocols. 2. **Implement More Commands:** Add handlers for all the MCP commands you want to support. 3. **Add Error Handling:** Make the server more robust by handling potential errors. 4. **Implement Concurrency:** Use threading or asynchronous operations to handle multiple clients. 5. **Integrate with a Media Player:** Connect the server to a media player library to control actual media playback. This detailed explanation and code example should give you a solid foundation for building your MCP server. Remember to consult the Dolphin MCP documentation for the most accurate and up-to-date information. Good luck!

Local Documents MCP Server

A Model Context Protocol server that allows AI assistants to discover, load, and process local documents on Windows systems, with support for multiple file formats and OCR capabilities for scanned PDFs.

searchAPI-mcp

A Model Context Protocol (MCP) based search API server that provides standardized access to Google Maps, Google Flights, Google Hotels and other services. This server enables AI assistants to access various search services through a unified interface.

MCP Dual-Cycle Reasoner

A Model Context Protocol server that empowers AI agents with metacognitive monitoring to detect reasoning loops and provide intelligent recovery using case-based reasoning and statistical analysis.

MCP Sandbox

Automatically converts JavaScript modules into MCP-compatible servers, making any JavaScript function accessible to AI systems through secure sandboxing with automatic type inference.

MLB V3 Scores MCP Server

An MCP Server that enables interaction with MLB scores and statistics via the SportsData.io MLB V3 Scores API, allowing users to access baseball data through natural language queries.

HubSpot MCP Server

Enables AI assistants to interact with HubSpot CRM for managing contacts, companies, deals, and sending emails through natural language commands.

Crownpeak DQM MCP Server

Enables quality checking and content management for websites through the Crownpeak DQM CMS REST API. Supports running quality checks, spellchecking, asset management, and checkpoint monitoring with both desktop and cloud deployment options.

burnish-example-server

Showcase MCP server for the Burnish Explorer — 34 tools across a fictional consulting company (projects, clients, team members, tasks, incidents, orders) that demonstrate every rendering path: cards, tables, charts, stat bars, pipelines, and multi-section dashboards. Install with npx burnish -- npx -y @burnishdev/example-server.

Date-time Tools MCP

Provides tools for date-time manipulation, including timezone conversion and arithmetic operations like adding or subtracting time units. It also enables users to retrieve current date, time, and timezone information.