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Share MCP - Model Context Protocol MCP Server导航站

Share MCP es un sitio de navegación centrado en el Protocolo de Contexto de Modelos (MCP). Ofrece una amplia gama de recursos, herramientas y servicios relacionados con MCP, clasificados para ayudar a los desarrolladores a encontrar rápidamente las soluciones MCP que necesitan.

mcp-client-and-server MCP server

Mirror of

Sefaria Jewish Library MCP Server

Espejo de

Code Reviewer Fixer Agent

This AI agent analyzes code repositories, detects potential security vulnerabilities, reviews code quality, and suggests fixes based on Sentry error logs using Sentry and GitHub MCP servers!

paloalto-mcp-servers

A collection of Model Context Protocol (MCP) servers for managing Palo Alto Networks firewalls and services

Gemini Flash MCP - Image Generation for Roo Code

MCP server for Google Gemini 2.0 Flash image generation

Deno 2 Playwright Model Context Protocol Server Example

Espejo de

mcp-excel

MCP server to give client the ability to read Excel files

Fillout.io MCP Server

Permite la gestión de formularios, el manejo de respuestas y el análisis a través de la API de Fillout.io para mejorar las interacciones y la información obtenida de los formularios.

Pandora's Shell

Servidor MCP que permite a los asistentes de IA ejecutar comandos de terminal a través de una interfaz de shell segura.

MCP GitHub

Repository created using GitHub MCP server

Memory MCP Server

Mirror of

Limitless MCP Integration

A Model Context Protocol server, client and interactive mode for Limitless API

postgres-mcp MCP server

Postgres Pro es un servidor de Protocolo de Contexto de Modelo (MCP) de código abierto creado para brindarte soporte a ti y a tus agentes de IA durante todo el proceso de desarrollo, desde la codificación inicial, pasando por las pruebas y la implementación, hasta el ajuste y el mantenimiento de la producción.

Goose FM

Okay, here's a breakdown of an MVP (Minimum Viable Product) for an MCP (Minecraft Protocol) server that allows AI assistants to "tune into" FM radio stations within Minecraft. This focuses on core functionality and avoids unnecessary complexity for the initial release. **Core Concept:** The AI assistant (e.g., a Python script using a library like `minecraft-protocol`) connects to the Minecraft server. It sends commands to interact with a virtual "radio" block. The server then streams audio from a real-world FM radio station to the AI assistant. **MVP Features:** 1. **Basic MCP Server:** * **Implementation:** Use a lightweight MCP server library (e.g., `node-minecraft-protocol` for Node.js, or `mcproto` for Python). Focus on handling basic connection and command parsing. * **Functionality:** * Accept connections from a single AI assistant client. * Authenticate the client (simple password or token-based authentication). * Handle basic chat messages (for debugging and status). 2. **Virtual Radio Block:** * **Representation:** A single, designated block in the Minecraft world (e.g., a specific type of block at a fixed coordinate). * **Interaction:** * The AI assistant sends a command to "interact" with the radio block (e.g., `/radio on`, `/radio off`, `/radio station <frequency>`). The server parses these commands. * The server sends feedback to the AI assistant (e.g., "Radio turned on", "Tuning to 98.7 FM", "Radio turned off"). This can be done via chat messages or custom packets. 3. **FM Radio Streaming:** * **Radio Source:** Use a reliable online FM radio streaming service or a local FM receiver connected to the server. Consider using a library like `vlc` or `ffmpeg` to capture the audio stream. * **Audio Encoding:** Encode the audio stream into a format suitable for transmission over the network (e.g., MP3, Opus). Keep the bitrate low to minimize bandwidth usage. * **Streaming to AI Assistant:** * Establish a separate TCP connection (or use WebSockets) between the server and the AI assistant for audio streaming. * Send audio data in small chunks to the AI assistant. * Implement basic error handling (e.g., reconnect if the stream is interrupted). 4. **AI Assistant Client (Example in Python):** ```python import minecraft_protocol import socket import threading import time SERVER_ADDRESS = ("localhost", 25565) # Replace with your server's address USERNAME = "AI_Assistant" PASSWORD = "password" # Replace with your password RADIO_STREAM_PORT = 12345 # Port for audio stream def receive_audio(sock): try: while True: data = sock.recv(1024) # Adjust buffer size as needed if not data: break # Process audio data (e.g., play it using a library like PyAudio) print(f"Received audio data: {len(data)} bytes") # Replace with actual audio playback except Exception as e: print(f"Error receiving audio: {e}") finally: sock.close() def main(): try: client = minecraft_protocol.Client(SERVER_ADDRESS[0], SERVER_ADDRESS[1], USERNAME) client.login(USERNAME, PASSWORD) print("Connected to Minecraft server.") # Connect to audio stream audio_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) audio_socket.connect((SERVER_ADDRESS[0], RADIO_STREAM_PORT)) print("Connected to audio stream.") audio_thread = threading.Thread(target=receive_audio, args=(audio_socket,)) audio_thread.daemon = True audio_thread.start() # Send commands to the server client.chat("/radio on") time.sleep(2) client.chat("/radio station 98.7") time.sleep(10) # Listen for a while client.chat("/radio off") client.close() print("Disconnected from Minecraft server.") except Exception as e: print(f"Error: {e}") if __name__ == "__main__": main() ``` **Technical Considerations:** * **Audio Latency:** Streaming audio over a network will introduce latency. Minimize this by using a low bitrate and efficient encoding. * **Bandwidth:** Consider the bandwidth requirements of streaming audio, especially if multiple AI assistants are connected. * **Error Handling:** Implement robust error handling to deal with network interruptions, invalid commands, and other potential issues. * **Security:** For the MVP, simple authentication is sufficient. For a production system, use more secure authentication methods. * **Scalability:** The MVP is designed for a single AI assistant. Consider scalability issues if you plan to support multiple clients. **Development Steps:** 1. **Set up the MCP server:** Choose a library and implement the basic server functionality (connection handling, authentication, chat). 2. **Implement the virtual radio block:** Define the block and handle the `/radio` commands. 3. **Implement the FM radio streaming:** Capture audio from a source, encode it, and stream it to the AI assistant. 4. **Develop the AI assistant client:** Connect to the server, send commands, and receive and play the audio stream. 5. **Test and debug:** Thoroughly test the system to identify and fix any issues. **Why this is an MVP:** * **Focus on core functionality:** It only implements the essential features needed to demonstrate the concept. * **Limited scope:** It supports a single AI assistant and a single radio block. * **Simplified implementation:** It uses simple authentication and error handling. * **Iterative development:** This MVP can be used as a foundation for adding more features and improving the system over time. **Possible Future Enhancements:** * **Multiple radio stations:** Allow the AI assistant to select from a list of available stations. * **Volume control:** Implement volume control for the radio. * **User interface:** Create a more user-friendly interface for the AI assistant. * **Multiple AI assistants:** Support multiple AI assistants connecting to the server. * **Integration with other Minecraft features:** Integrate the radio with other Minecraft features, such as redstone circuits. * **Spatial Audio:** Make the audio louder the closer you are to the radio block. This MVP provides a solid starting point for building a more complex and feature-rich system. Remember to prioritize simplicity and focus on delivering a working product as quickly as possible. Good luck!

MCP Server

(STDIO) Model Context Protocol (MCP) servers designed for local execution

Quarkus Model Context Protocol (MCP) Server

Mirror of

Waldzell MCP Servers

Monorepositorio de servidores MCP de Waldzell AI. ¡Se usa en Claude Desktop, Cline, Roo Code y más!

My Slack MCP Server Extension

Una extensión para el servidor MCP de Slack.

Microsoft SQL Server MCP Server

Espejo de

Rootly MCP Server

Mirror of

Data BI MCP Server

Un servidor MCP (Protocolo de Contexto de Modelo) para la transformación de datos y gráficos de BI permitirá a los asistentes de IA conectarse a sus fuentes de datos, transformar datos y generar visualizaciones de alta calidad a través de solicitudes en lenguaje natural.

Bishop MCP (Master Control Program)

Aquí tienes un script avanzado para servidor MCP que he desarrollado y me gustaría compartir.

Coinmarket MCP server

Mirror of

mcp-server-proxy

Converts MCP protocol's SSE transport layer to a standard HTTP request/response.

Google Analytics MCP Server

Espejo de

Cortellis MCP Server

An MCP server enabling AI assistants to search and analyze pharmaceutical data through Cortellis. Features comprehensive drug search, ontology exploration, and real-time clinical trial data access.

WIP: MCP Server Superset

Un servidor de Protocolo de Contexto de Modelo que permite a los modelos de lenguaje grandes interactuar con bases de datos Apache Superset a través de la API REST, admitiendo consultas de bases de datos, búsquedas de tablas, recuperación de información de campos y ejecución de SQL.