Discover Awesome MCP Servers

Mailmodo

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.

Stata MCP Server

Fornece uma ponte entre o software estatístico Stata e editores de código como VS Code e Cursor, permitindo que os usuários executem comandos Stata diretamente do editor, visualizem a saída em tempo real e obtenham assistência com tecnologia de IA para codificação em Stata.

Sequential Thinking MCP Server

Fornece uma ferramenta para resolução de problemas dinâmica e reflexiva, dividindo problemas complexos em etapas gerenciáveis, com suporte para revisão, ramificação e geração de hipóteses.

very-simple-mcp-server-sample

A amostra de servidor MCP mais simples do mundo, talvez.

CB Insights MCP Server

An interface that allows developers to interact with ChatCBI LLM through AI Agents, providing access to CB Insights' conversational AI capabilities.

Webex Mcp Server

Um servidor MCP NodeJS para interagir com Espaços Webex.

buttplug-mcp - Buttplug.io MCP Server

Servidor do Protocolo de Contexto de Modelo (MCP) Buttplug.io

Claude Crew 🤖

Uma ferramenta CLI para aprimorar o Claude Desktop com capacidades e fluxos de trabalho adicionais.

WhatsApp Web MCP

Uma ponte que conecta o WhatsApp Web a modelos de IA usando o Protocolo de Contexto de Modelo, permitindo que Claude e outros sistemas de IA interajam com o WhatsApp através de uma interface padronizada.

Model Context Protocol PostgreSQL Server

Um servidor que permite que modelos de IA interajam com bancos de dados PostgreSQL através de um protocolo padronizado, fornecendo informações sobre o esquema do banco de dados e capacidades de execução de consultas SQL.

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.

MCP Document Reader

Um servidor de Protocolo de Contexto de Modelo (MCP) que permite a interação com documentos PDF e EPUB, projetado para funcionar com o IDE Windsurf da Codeium.

MCP SSE Client Python

Cliente MCP simples para servidores MCP remotos 🌐

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.

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!

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.

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

Um servidor que fornece ferramentas para obter informações de preços em tempo real para vários ativos, incluindo metais preciosos e criptomoedas, permitindo que modelos de linguagem acessem e exibam dados de preços de ativos atuais.

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

Um servidor MCP que fornece ferramentas para carregar e buscar documentação de qualquer fonte llms.txt, dando aos usuários controle total sobre a recuperação de contexto para LLMs em agentes e aplicações IDE.

MRP Calculator MCP Server

Fornece ferramentas de Planejamento das Necessidades de Materiais (MRP) para calcular cronogramas de entrega, determinar necessidades de pedidos e realizar cálculos de período de MRP com base nos níveis de estoque, previsões e restrições de 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.

Perplexity MCP Server

Um servidor de Protocolo de Contexto de Modelo (MCP) da API Perplexity que desbloqueia as capacidades de IA aumentadas por pesquisa da Perplexity para agentes LLM. Apresenta tratamento robusto de erros, validação de entrada segura e raciocínio transparente com o parâmetro showThinking. Construído com segurança de tipo, arquitetura modular e utilitários prontos para produção.

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.