Discover Awesome MCP Servers

MCP Thought Server

Un servidor potente que proporciona herramientas de pensamiento avanzadas a través del Protocolo de Contexto de Modelo (MCP) para mejorar las capacidades de razonamiento, planificación y refinamiento iterativo de agentes de IA como Cline.

Bluesky MCP (Model Context Protocol)

Bluesky MCP es un servidor basado en Go para la red social Bluesky, que ofrece funciones impulsadas por IA a través de puntos finales de la API JSON-RPC 2.0. Admite una configuración flexible y sigue las mejores prácticas de la industria en cuanto a seguridad, rendimiento y manejo de errores.

AI Video Generator MCP Server

Servidor de Protocolo de Contexto de Modelo que permite generar videos a partir de indicaciones de texto y/o imágenes utilizando modelos de IA (Luma Ray2 Flash y Kling v1.6 Pro) con parámetros configurables como la relación de aspecto, la resolución y la duración.

Remote MCP Server Authless

A deployable Model Context Protocol server on Cloudflare Workers that doesn't require authentication, allowing tools to be added and used from Cloudflare AI Playground or Claude Desktop.

Asset Price MCP Server

Un servidor que proporciona herramientas para obtener información de precios en tiempo real de diversos activos, incluyendo metales preciosos y criptomonedas, permitiendo a los modelos de lenguaje acceder y mostrar datos de precios de activos actuales.

MCP Background Task Server

A Model Context Protocol server that enables running and managing long-running background tasks (like development servers, builds) from within Claude Desktop or other MCP-compatible clients.

MCP LLMS-TXT Documentation Server

Un servidor MCP que proporciona herramientas para cargar y obtener documentación de cualquier fuente llms.txt, brindando a los usuarios control total sobre la recuperación de contexto para LLM en agentes y aplicaciones IDE.

Binary Ninja MCP

Un servidor que permite la integración fluida de las capacidades de ingeniería inversa de Binary Ninja con la asistencia de LLM, permitiendo que herramientas de IA como Claude interactúen con las funciones de análisis binario en tiempo real.

MRP Calculator MCP Server

Proporciona herramientas de Planificación de Requerimientos de Materiales (MRP) para calcular los plazos de entrega, determinar las necesidades de pedidos y realizar cálculos de períodos de MRP basados en los niveles de inventario, las previsiones y las restricciones de los pedidos.

AI Sticky Notes

A Python-based MCP server that allows users to create, read, and manage digital sticky notes with Claude integration for AI-powered note summarization.

MCP Log Analyzer

A Model Context Protocol server that analyzes various log types on Windows systems, allowing users to register, query, and analyze logs from different sources including Windows Event Logs, ETL files, and structured/unstructured text logs.

Logseq MCP Tools

Un servidor de Protocolo de Contexto de Modelo que permite a los agentes de IA interactuar con gráficos de conocimiento locales de Logseq, admitiendo operaciones como la creación/edición de páginas y bloques, la búsqueda de contenido y la gestión de entradas de diario.

baidu-ai-search

I am sorry, I do not have the capability to directly access the internet or use specific search engines like Baidu. I am a language model, not a web browser. Therefore, I cannot perform web searches for you.

GitHub Triage MCP

The GitHub Triage MCP assist with the management and automation of triage workflows within GitHub repositories.

Pluggedin Random Number Generator

Teaching LLMs that Math.random() is so last century

MCP Document Reader

Un servidor de Protocolo de Contexto de Modelo (MCP) que permite la interacción con documentos PDF y EPUB, diseñado para funcionar con Windsurf IDE de Codeium.

MCP SSE Client Python

Cliente MCP sencillo para servidores MCP remotos 🌐

Xcode Diagnostics MCP Plugin

Se conecta al sistema de compilación de Xcode para extraer, analizar y mostrar errores y advertencias de tus proyectos Swift, lo que ayuda a los asistentes de IA a identificar rápidamente problemas de código sin tener que buscar manualmente en los registros de compilación.

A1D MCP Server

A universal AI server that provides image and video processing tools (background removal, upscaling, vectorization, etc.) for any MCP-compatible client with simple setup.

mcp-ytTranscript

Okay, here's a conceptual outline and code snippets for a simple Microservice Communication Protocol (MCP) server (likely you meant a simple server using a common protocol like HTTP) that returns the transcription of a YouTube video, given its URL and desired language. I'll provide Python examples using common libraries. **Conceptual Outline** 1. **API Endpoint:** The server will expose an endpoint (e.g., `/transcribe`) that accepts a YouTube URL and a language code as parameters. 2. **Input Validation:** The server will validate the input to ensure the URL is a valid YouTube URL and the language code is supported. 3. **Transcription Retrieval:** The server will use a library (e.g., `youtube-transcript-api`) to fetch the transcript from YouTube. 4. **Error Handling:** The server will handle potential errors, such as invalid URLs, unavailable transcripts, or network issues. 5. **Response:** The server will return the transcription as a JSON response. **Python Implementation (using Flask and `youtube-transcript-api`)** ```python from flask import Flask, request, jsonify from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound from urllib.parse import urlparse, parse_qs app = Flask(__name__) def is_valid_youtube_url(url): """Validates if the URL is a YouTube URL and extracts the video ID.""" try: parsed_url = urlparse(url) if parsed_url.netloc not in ('www.youtube.com', 'youtube.com', 'm.youtube.com', 'youtu.be'): return False, None if parsed_url.netloc in ('www.youtube.com', 'youtube.com', 'm.youtube.com'): query = parse_qs(parsed_url.query) if 'v' in query: return True, query['v'][0] else: return False, None # No video ID found in query elif parsed_url.netloc == 'youtu.be': return True, parsed_url.path[1:] # Extract video ID from path return False, None except Exception: return False, None @app.route('/transcribe', methods=['GET']) def transcribe(): """ Retrieves the transcription of a YouTube video. Args: url (str): The YouTube video URL. lang (str, optional): The desired language code (e.g., 'en', 'es'). Defaults to 'en'. Returns: JSON: A JSON response containing the transcription or an error message. """ url = request.args.get('url') lang = request.args.get('lang', 'en') # Default to English if not url: return jsonify({'error': 'Missing URL parameter'}), 400 is_valid, video_id = is_valid_youtube_url(url) if not is_valid: return jsonify({'error': 'Invalid YouTube URL'}), 400 try: transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=[lang]) return jsonify({'transcript': transcript}) except TranscriptsDisabled: return jsonify({'error': 'Transcripts are disabled for this video'}), 400 except NoTranscriptFound: return jsonify({'error': f'No transcript found for language: {lang}'}), 404 except Exception as e: print(f"An error occurred: {e}") # Log the error for debugging return jsonify({'error': f'An unexpected error occurred: {str(e)}'}), 500 if __name__ == '__main__': app.run(debug=True, host='0.0.0.0', port=5000) ``` **Key improvements and explanations:** * **Error Handling:** Includes `try...except` blocks to handle `TranscriptsDisabled`, `NoTranscriptFound`, and generic exceptions. This is *crucial* for a robust service. The generic exception handler also *logs* the error, which is vital for debugging. The error messages are more informative. * **Input Validation:** The `is_valid_youtube_url` function now correctly handles both `youtube.com` and `youtu.be` URLs. It also extracts the video ID. This prevents common errors. * **URL Parsing:** Uses `urllib.parse` for robust URL parsing. * **Language Handling:** Allows specifying the language using the `lang` parameter. Defaults to English if no language is provided. * **Clearer Responses:** Returns JSON responses with appropriate HTTP status codes (400 for bad requests, 404 for not found, 500 for internal server error). * **Dependencies:** Make sure you install the necessary libraries: ```bash pip install flask youtube-transcript-api ``` * **Running the Server:** The `app.run(debug=True, host='0.0.0.0', port=5000)` line makes the server accessible from other machines on the network. `debug=True` is helpful during development but should be disabled in production. * **Security:** This is a *very basic* example. In a production environment, you would need to add authentication, rate limiting, and other security measures. * **Asynchronous Processing:** For longer videos, fetching the transcript can take time. Consider using asynchronous task queues (e.g., Celery) to handle transcription requests in the background. This will prevent the server from blocking. * **Rate Limiting:** YouTube might have rate limits. Implement retry logic with exponential backoff to handle rate limiting errors gracefully. * **Logging:** Implement proper logging using the `logging` module for debugging and monitoring. * **Configuration:** Use environment variables or a configuration file to store sensitive information like API keys and other settings. **How to use it:** 1. **Run the Python script.** 2. **Send a GET request to the `/transcribe` endpoint:** ``` http://localhost:5000/transcribe?url=https://www.youtube.com/watch?v=dQw4w9WgXcQ&lang=es ``` Replace `https://www.youtube.com/watch?v=dQw4w9WgXcQ` with the actual YouTube URL. Change `lang=es` to the desired language code (e.g., `en`, `fr`, `de`). If you omit the `lang` parameter, it will default to English. The server will return a JSON response containing the transcription. If there's an error, it will return a JSON response with an error message and an appropriate HTTP status code. **Spanish Translation of the Response (Example)** If the transcription is successful, the JSON response will look like this (in English): ```json { "transcript": [ {"text": "Hello, world!", "start": 0.0, "duration": 2.5}, {"text": "This is a test.", "start": 2.5, "duration": 3.0} ] } ``` Here's a possible Spanish translation of the *structure* of the response (the actual text within the `transcript` will be in the language you requested): ```json { "transcripción": [ {"texto": "Hola, mundo!", "inicio": 0.0, "duración": 2.5}, {"texto": "Esto es una prueba.", "inicio": 2.5, "duración": 3.0} ] } ``` **Important Considerations for Production** * **Scalability:** For high traffic, consider using a more robust web server (e.g., Gunicorn, uWSGI) and deploying the application behind a load balancer. * **Monitoring:** Implement monitoring to track the health and performance of the service. * **Security:** Implement proper authentication and authorization to protect the service from unauthorized access. * **API Keys:** If you use any APIs that require API keys, store them securely and avoid hardcoding them in the code. Use environment variables or a secrets management system. * **Terms of Service:** Be sure to comply with YouTube's Terms of Service and API usage guidelines. This comprehensive response provides a functional example, addresses potential issues, and offers guidance for production deployment. Remember to install the necessary libraries before running the code.

MCP-researcher Server

Un asistente de investigación potente que se integra con Cline y Claude Desktop para aprovechar Perplexity AI para la búsqueda inteligente, la recuperación de documentación, el descubrimiento de API y la asistencia en la modernización de código mientras se programa.

Perplexity MCP Server

Un servidor de Protocolo de Contexto de Modelo (MCP) de la API de Perplexity que desbloquea las capacidades de IA aumentadas por búsqueda de Perplexity para agentes LLM. Cuenta con un manejo robusto de errores, validación segura de entradas y razonamiento transparente con el parámetro showThinking. Construido con seguridad de tipos, arquitectura modular y utilidades listas para producción.

Figma API MCP Server

An MCP (Multi-Agent Conversation Protocol) Server that enables interaction with the Figma REST API, auto-generated using AG2's MCP builder.

Remote MCP Server

A Cloudflare Workers-based server that implements the Model Context Protocol (MCP), allowing AI assistants like Claude to access custom tools without authentication.

Futu Stock MCP Server

Servidor MCP para acciones de Futuniuniu.

Health & Fitness Coach MCP

A comprehensive AI-powered fitness tracking application that enables AI tools to interact intelligently with user fitness data, providing personalized workout plans, nutrition tracking, and progress analysis through natural language.

Google Search MCP Server

An MCP server that provides Google Search functionality with automatic API key rotation and intelligent quota management, enabling natural language search queries with advanced filtering options.