Discover Awesome MCP Servers

Asterisk S2S MCP Server

MCP Server for automated conversational phone calls using Asterisk with Speech-to-Speech capabilities, allowing users to make phone conversations as easily as writing a prompt.

Top MCP Servers

Uma coleção selecionada dos principais servidores de Protocolo de Contexto de Modelo (MCP) para aprimorar fluxos de trabalho de desenvolvimento em 2025.

MCP Subfinder Server

Servidor Model Context Protocol (MCP) que envolve a ferramenta subfinder da ProjectDiscovery para uma enumeração de subdomínios poderosa através de uma 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.

atlassian-mcp-server

An open-source Model Context Protocol (MCP) server for Atlassian Jira and Confluence Cloud, enabling LLMs to search, read, write, and manage issues and pages.

LangSmith MCP Server

Enables language models to access LangSmith observability platform features including fetching conversation history, managing prompts, retrieving traces and runs, working with datasets and examples, and analyzing experiments.

Brazilian ZIP Code Lookup

Enables lookup of Brazilian addresses by CEP (postal code) using the ViaCEP API, returning formatted address information including street, neighborhood, city, and state.

Logseq MCP Tools

Um servidor de Protocolo de Contexto de Modelo que permite que agentes de IA interajam com grafos de conhecimento Logseq locais, suportando operações como criar/editar páginas e blocos, pesquisar conteúdo e gerenciar entradas de diário.

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.

NotionMCP

Enables AI assistants to search, read, summarize, and analyze sentiment of Notion pages and databases, turning your Notion workspace into an intelligent, queryable knowledge system.

ssh-mcp-server

Enables secure remote command execution and bidirectional file transfers on SSH servers through the Model Context Protocol. It features robust security controls including command whitelisting, credential isolation, and support for multiple SSH connection profiles.

FastAPI MCP SSE

Demonstrates how to integrate Model Context Protocol with Server-Sent Events (SSE) in a FastAPI web application, including a weather service example with tools for getting forecasts and alerts.

HC3 MCP Server

Enables AI assistants to interact with Fibaro Home Center 3 smart home systems through natural language commands. Provides comprehensive device control, scene management, QuickApp development, and system monitoring capabilities via the HC3 REST API.

Financial MCP Server

Provides access to real-time currency exchange rates, live stock market data via Alpha Vantage, and local transaction analysis from CSV databases. It enables AI assistants to perform currency conversions, stock comparisons, and budget tracking through natural language.

Pluggedin Random Number Generator

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

PCILeech MCP Server

Enables AI assistants to perform DMA-based memory operations through PCILeech hardware using natural language commands, supporting memory reading, writing, and multi-format visualization for debugging and security research.

MCP SSE Client Python

Cliente MCP simples para servidores MCP remotos 🌐

Deep Search MCP Server

Provides comprehensive search capabilities including web search, content extraction, news search, academic search, and AI-powered multi-source research. Enables natural language access to web content and research through a production-ready MCP server.

Xcode Diagnostics MCP Plugin

Conecta-se ao sistema de compilação do Xcode para extrair, analisar e exibir erros e avisos dos seus projetos Swift, ajudando assistentes de IA a identificar rapidamente problemas de código sem precisar procurar manualmente nos logs de compilação.

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.

Notion MCP Server

Enables interaction with Notion databases through the Notion API, supporting full CRUD operations on pages and databases. Supports advanced querying, filtering, sorting, and all property types with Docker deployment for easy integration with Cursor and Claude.

Hue MCP Server

Enables AI assistants to interact with Hadoop Hue for executing SQL queries using Hive, SparkSQL, or Impala and managing HDFS files. It supports directory browsing, file transfers, and exporting query results to CSV through the Model Context Protocol.

mcp-ytTranscript

Okay, here's a conceptual outline and code snippets for a simple MCP (presumably meaning Minimal Complete and Verifiable) server in Python that transcribes YouTube videos, along with explanations and considerations for Portuguese: **Conceptual Outline** 1. **Server Framework:** Use a lightweight framework like Flask or FastAPI to create a simple API endpoint. 2. **YouTube Video Download:** Use `yt-dlp` (a fork of `youtube-dl`) to download the video's audio. `yt-dlp` is generally preferred as it's actively maintained. 3. **Audio Transcription:** Use a speech-to-text library like `Whisper` (from OpenAI) or `SpeechRecognition` (which can use various APIs like Google Cloud Speech-to-Text). Whisper is often preferred for its quality and ability to run locally. 4. **Language Handling:** Specify the desired language for transcription. 5. **Error Handling:** Handle potential errors (invalid URLs, download failures, transcription errors). 6. **Return Transcription:** Return the transcription as a JSON response. **Code Snippets (Python with Flask and Whisper)** ```python from flask import Flask, request, jsonify import yt_dlp import whisper import os import tempfile app = Flask(__name__) # Load the Whisper model (choose a size based on your needs and resources) model = whisper.load_model("base") # Options: tiny, base, small, medium, large def transcribe_youtube_video(youtube_url, language="en"): """ Downloads audio from a YouTube video and transcribes it using Whisper. Args: youtube_url: The URL of the YouTube video. language: The desired language for transcription (e.g., "en" for English, "pt" for Portuguese). Returns: The transcription as a string, or None if an error occurred. """ try: # 1. Download Audio using yt-dlp ydl_opts = { 'format': 'bestaudio/best', 'extractaudio': True, 'audioformat': 'mp3', 'outtmpl': '%(id)s.%(ext)s', # Save audio as video_id.mp3 'noplaylist': True, } with yt_dlp.YoutubeDL(ydl_opts) as ydl: info_dict = ydl.extract_info(youtube_url, download=True) video_id = info_dict.get('id', None) audio_file = f"{video_id}.mp3" # 2. Transcribe Audio using Whisper result = model.transcribe(audio_file, language=language) transcription = result["text"] # 3. Clean up the audio file os.remove(audio_file) return transcription except Exception as e: print(f"Error: {e}") return None @app.route('/transcribe', methods=['POST']) def transcribe_endpoint(): """ API endpoint to transcribe a YouTube video. """ data = request.get_json() youtube_url = data.get('url') language = data.get('language', 'en') # Default to English if not youtube_url: return jsonify({'error': 'Missing YouTube URL'}), 400 transcription = transcribe_youtube_video(youtube_url, language) if transcription: return jsonify({'transcription': transcription}) else: return jsonify({'error': 'Transcription failed'}), 500 if __name__ == '__main__': app.run(debug=True) # Don't use debug=True in production! ``` **How to Run:** 1. **Install Dependencies:** ```bash pip install Flask yt-dlp openai-whisper ``` 2. **Set OpenAI API Key (if needed):** If you're using the OpenAI Whisper API directly (less common now that the `whisper` library provides local models), you'll need to set your API key as an environment variable: ```bash export OPENAI_API_KEY="YOUR_OPENAI_API_KEY" ``` 3. **Run the Script:** ```bash python your_script_name.py ``` 4. **Send a Request:** Use `curl`, `Postman`, or similar to send a POST request to `http://127.0.0.1:5000/transcribe` with a JSON payload: ```bash curl -X POST -H "Content-Type: application/json" -d '{"url": "YOUR_YOUTUBE_URL", "language": "pt"}' http://127.0.0.1:5000/transcribe ``` **Explanation and Portuguese Considerations** * **`yt-dlp`:** Downloads the audio from the YouTube video. It's crucial for getting the audio data. * **`whisper`:** Performs the speech-to-text transcription. The `language` parameter is key for getting accurate results in Portuguese. The model size ("base" in the example) affects accuracy and resource usage. Larger models are generally more accurate but require more memory and processing power. Experiment to find the best balance for your needs. * **Language Code:** Use `"pt"` for Portuguese. Whisper supports many languages. * **Flask:** Provides the web server functionality to receive requests and send responses. * **Error Handling:** The `try...except` block is important to catch potential errors during the download or transcription process. More robust error handling might involve logging errors to a file. * **Temporary Files:** The audio file is saved temporarily and then deleted. This prevents your server from filling up with audio files. Consider using `tempfile.NamedTemporaryFile` for more secure temporary file handling. * **API Endpoint:** The `/transcribe` endpoint receives the YouTube URL and language code in a JSON payload. * **JSON Response:** The transcription is returned as a JSON response, making it easy to parse by other applications. **Important Notes and Improvements** * **Model Size:** The `whisper.load_model()` line is critical. Choose the appropriate model size based on your hardware and accuracy requirements. "tiny" is the fastest but least accurate. "large" is the most accurate but requires significant resources. * **Resource Usage:** Transcription can be resource-intensive. Consider using a more robust server setup (e.g., using a WSGI server like Gunicorn or uWSGI) if you expect a high volume of requests. * **Asynchronous Processing:** For better performance, especially with longer videos, consider using asynchronous task queues (like Celery or Redis Queue) to offload the transcription process to a background worker. This will prevent the API from blocking while the transcription is running. * **Rate Limiting:** Implement rate limiting to prevent abuse of your API. * **Security:** In a production environment, you'll need to consider security best practices, such as input validation, authentication, and authorization. * **Portuguese Dialects:** If you need to support specific Portuguese dialects (e.g., Brazilian Portuguese), you might need to fine-tune the Whisper model or use a different speech-to-text engine that is specifically trained on that dialect. Whisper generally handles different dialects reasonably well, but fine-tuning can improve accuracy. * **Subtitles/Timestamps:** Whisper can also generate subtitles with timestamps. If you need subtitles, you can modify the code to extract the subtitle information from the Whisper result. * **Google Cloud Speech-to-Text:** If you need very high accuracy, especially for specific domains or accents, consider using Google Cloud Speech-to-Text. It's a paid service, but it often provides better results than open-source alternatives. You would need to install the `google-cloud-speech` library and authenticate with Google Cloud. The `SpeechRecognition` library can be used to interface with Google Cloud Speech-to-Text. This comprehensive response provides a solid foundation for building your YouTube transcription server. Remember to adapt the code and configuration to your specific needs and environment. Good luck!

MCP-researcher Server

Um assistente de pesquisa poderoso que se integra com Cline e Claude Desktop para aproveitar o Perplexity AI para busca inteligente, recuperação de documentação, descoberta de API e assistência na modernização de código durante a programação.

Bluesky MCP (Model Context Protocol)

Bluesky MCP é um servidor baseado em Go para a rede social Bluesky, oferecendo recursos alimentados por IA através de endpoints de API JSON-RPC 2.0. Ele suporta configuração flexível e segue as melhores práticas da indústria para segurança, desempenho e tratamento de erros.

AI Video Generator MCP Server

Servidor de Protocolo de Contexto de Modelo que permite gerar vídeos a partir de prompts de texto e/ou imagens usando modelos de IA (Luma Ray2 Flash e Kling v1.6 Pro) com parâmetros configuráveis como proporção, resolução e duração.