Discover Awesome MCP Servers

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Mailmodo

Mureka MCP Server

A Model Context Protocol server that enables AI assistants like Claude to generate lyrics, songs, and background music through Mureka's APIs.

Remote MCP Server Authless

Deploys a Model Context Protocol server on Cloudflare Workers without authentication requirements, allowing users to create and access custom AI tools from Claude Desktop or the Cloudflare AI Playground.

Ayrshare Unofficial MCP Server

Enables AI agents to interact with the Ayrshare API to publish social media posts, manage profiles, and handle comments or messages. It supports executing real-time API calls for analytics, media uploads, and automated scheduling across various social platforms.

FGD Fusion Stack Pro

MCP server with intelligent memory management and file monitoring that enables context-aware AI assistance across multiple LLM providers (Grok, OpenAI, Claude, Ollama) with persistent memory of interactions and real-time file system changes.

Universal Menu

Provides an interactive decision menu that surfaces contextual choices on every assistant turn, allowing users to navigate available actions through a React widget interface.

pocket-joe-mcp-toys

Enables transcription of YouTube videos to extract video titles, full transcripts, thumbnail URLs, and video IDs. Built with pocket-joe and deployable to Railway for scalable MCP server hosting.

HireScript MCP Server

Generates inclusive, bias-free job descriptions by leveraging Claude AI to detect and mitigate gendered, ageist, or ability-biased language. It provides structured feedback through inclusivity scoring and specific alternative suggestions for exclusionary phrases.

Zava Insurance MCP Server

Enables management of insurance claims, inspections, and contractors through interactive UI widgets and data tools. Users can view claim dashboards, update statuses, and query service provider information using natural language.

Datadog MCP Server

Enables interaction with Datadog's monitoring platform to search logs, search trace spans, and perform trace span aggregation for analysis.

WhatsApp Web MCP

Un puente que conecta WhatsApp Web con modelos de IA utilizando el Protocolo de Contexto del Modelo, permitiendo que Claude y otros sistemas de IA interactúen con WhatsApp a través de una interfaz estandarizada.

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.

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.

Ethora MCP Server

Enables integration with the Ethora platform through user authentication, registration, and application management operations. Supports creating, updating, deleting, and listing applications within the Ethora service.

AroFlo MCP

A production-ready MCP server that enables interaction with the AroFlo API to manage quotes, projects, and labor reporting. It features secure request signing and exposes API documentation as resources for enhanced context and tool surface.

Algorand MCP Server

Enables interaction with the Algorand blockchain network including account management, payments, asset creation and transfers, along with general utility tools. Provides secure mnemonic encryption and supports both testnet and mainnet environments.

MCP Math Server

A Node.js server that processes mathematical calculations and natural language math queries through RESTful API endpoints.

OpenEHR MCP Server

Enables LLMs to interact with OpenEHR electronic health record systems by retrieving clinical archetypes, generating AQL queries from natural language, and executing queries against on-premise EHR databases. Supports OpenEHR Clinical Knowledge Manager integration and custom IIS-hosted EHR APIs for comprehensive medical data access.

Telephony MCP Server

Enables LLM applications to make voice calls and send SMS messages through the Vonage API, allowing AI assistants to perform real-world telephony operations with support for speech recognition and customizable voice parameters.

Top MCP Servers

Una selección curada de los mejores servidores de Protocolo de Contexto de Modelo (MCP) para mejorar los flujos de trabajo de desarrollo en 2025.

MCP Subfinder Server

Servidor de Protocolo de Contexto de Modelo (MCP) que envuelve la herramienta Subfinder de ProjectDiscovery para una potente enumeración de subdominios a través de una API JSON-RPC.

VN Stock API MCP Server

Provides access to Vietnamese stock market data and APIs from VNDirect, FireAnt, and SSI, including real-time stock prices, market news from CafeF, technical analysis (Doji patterns), and comprehensive stock listings.

MCP Node Time

A MCP server that provides timezone-aware date and time operations. This server addresses the common issue where AI assistants provide incorrect date information due to timezone confusion.

A2A Registry

Provides a unified registry for discovering and managing agents that implement the A2A (Agent-to-Agent) protocol, enabling registration, querying, and CRUD operations on agent metadata through both REST API and MCP tools.