Discover Awesome MCP Servers

Matrix Pattern MCP Server

Enables advanced pattern management and synchronization for development workflows using a two-dimensional matrix structure. Organizes patterns by development phases and feature domains with intelligent horizontal/vertical synchronization capabilities.

MCP-BOS

Um framework de servidor modular e extensível do Protocolo de Contexto de Modelo, projetado para o Claude Desktop, que utiliza a descoberta automática de módulos baseada em convenções para estender facilmente a funcionalidade de aplicações de IA sem modificar o código principal.

RocketReach MCP Server

A Model Context Protocol server that connects to RocketReach API, enabling AI assistants to find professional/personal emails, phone numbers, and enrich company data.

Bitbucket MCP

A Model Context Protocol server that enables AI assistants to interact with Bitbucket repositories, pull requests, and other resources through Bitbucket Cloud and Server APIs.

MCP Server Go

Uma implementação simples de servidor MCP escrita em Go.

Apollo.io MCP Server

Servidor MCP que expõe as funcionalidades da API Apollo.io como ferramentas.

PayPal MCP Server

A Python implementation that enables Large Language Models to interact with PayPal's APIs through function calling, supporting features like invoices, orders, products, subscriptions, and transactions.

Knowledge MCP Server

Provides centralized knowledge management for projects, allowing users to store, search, and maintain project-specific knowledge that persists across sessions.

MCP Weather Server

Um servidor de dados meteorológicos especializado, construído usando o MCP SDK e TypeScript, que fornece informações meteorológicas através do Protocolo de Contexto de Modelo (MCP). Este projeto permite que Grandes Modelos de Linguagem que suportam MCP acessem dados meteorológicos diretamente, criando uma integração perfeita entre sistemas de IA e informações meteorológicas em tempo real.

mcp_repo_9610b307

Este é um repositório de teste criado pelo script de teste do Servidor MCP para o GitHub.

QuickBooks MCP Server by CData

QuickBooks MCP Server by CData

Dub.co Link Shortener Server

Permite que agentes de IA criem, atualizem e gerenciem links curtos através da sua conta Dub.co, possibilitando a criação, modificação e exclusão de URLs personalizados encurtados.

Gitingest MCP Server

Gitingest MCP Server

MCP Proxy Server

Um servidor proxy MCP que agrega e serve múltiplos servidores de recursos MCP através de um único servidor HTTP.

Magic Component Platform

Ferramenta baseada em IA que ajuda desenvolvedores a criar componentes de UI bonitos instantaneamente através de descrições em linguagem natural, integrando-se com IDEs populares como Cursor, Windsurf e VSCode.

mcp-talib

Um servidor de Protocolo de Contexto de Modelo (MCP) que fornece as funções do ta-lib-python.

Doxygen MCP Server

A comprehensive server that enables AI assistants to generate, configure, and manage Doxygen documentation for various programming languages through a clean interface.

Geekbot MCP

Um servidor que conecta a IA Claude da Anthropic com as ferramentas de gestão de stand-up do Geekbot, permitindo que os usuários acessem e utilizem dados do Geekbot dentro das conversas do Claude.

Zerops Documentation MCP Server

Um servidor provedor de contexto gerenciado que rastreia e indexa a documentação do Zerops, tornando-a disponível como uma fonte de contexto pesquisável para o Cursor IDE.

google-sheets-mcp

Your AI Assistant's Gateway to Google Sheets! 25 powerful tools for seamless Google Sheets automation via MCP

Airtable MCP

Conecta ferramentas de IA diretamente ao Airtable, permitindo que os usuários consultem, criem, atualizem e excluam registros usando linguagem natural.

PhoneLCDParts MCP Server

A web scraping server that retrieves product information (name, price, URL, image) from phonelcdparts.com for any search query.

Mcp_trial

Testando o servidor MCP e está funcionando.

MCP Server for Awesome-llms-txt

Okay, I understand. You want me to create an MCP (presumably referring to a "Minimal, Complete, and Verifiable" example) server setup for the `SecretiveShell/Awesome-llms-txt` project, and to document the process directly within this conversation, using MCP resources. This is a bit abstract, as I don't have direct access to your file system or the `SecretiveShell/Awesome-llms-txt` project. Therefore, I'll provide a *conceptual* MCP, focusing on the core elements and assuming a basic understanding of Python and server setup. You'll need to adapt this to your specific environment and project structure. **Conceptual MCP: A Simple API Server for Serving Text from `Awesome-llms-txt`** This MCP will focus on: 1. **Loading Text Data:** How to load text data from a file (assuming `Awesome-llms-txt` contains text files). 2. **A Minimal API Endpoint:** A single API endpoint that returns the content of a specific text file. 3. **Basic Server Setup (using Flask):** A simple Flask server to host the API. 4. **Documentation within the Code:** Docstrings and comments to explain the code. **Code (Python with Flask):** ```python from flask import Flask, jsonify, abort import os app = Flask(__name__) # --- Configuration --- TEXT_DIRECTORY = "path/to/your/Awesome-llms-txt/text_files" # Replace with the actual path ALLOWED_EXTENSIONS = ['.txt'] # --- Helper Functions --- def is_valid_file(filename): """ Checks if a filename is valid based on allowed extensions. Args: filename (str): The name of the file. Returns: bool: True if the file is valid, False otherwise. """ return any(filename.endswith(ext) for ext in ALLOWED_EXTENSIONS) def load_text_file(filename): """ Loads the content of a text file. Args: filename (str): The name of the file to load. Returns: str: The content of the file, or None if the file doesn't exist or is invalid. """ filepath = os.path.join(TEXT_DIRECTORY, filename) if not os.path.exists(filepath) or not is_valid_file(filename): return None try: with open(filepath, 'r', encoding='utf-8') as f: return f.read() except Exception as e: print(f"Error reading file: {e}") # Log the error return None # --- API Endpoints --- @app.route('/text/<filename>', methods=['GET']) def get_text(filename): """ API endpoint to retrieve the content of a text file. Args: filename (str): The name of the text file to retrieve. Returns: JSON: A JSON response containing the text content, or an error message. """ text_content = load_text_file(filename) if text_content is None: abort(404, description="File not found or invalid.") # Return a 404 error return jsonify({'filename': filename, 'content': text_content}) # --- Error Handling --- @app.errorhandler(404) def resource_not_found(e): """ Custom error handler for 404 errors. """ return jsonify(error=str(e)), 404 # --- Main Execution --- if __name__ == '__main__': app.run(debug=True) # Enable debug mode for development ``` **Explanation and Documentation (MCP Resources):** * **`TEXT_DIRECTORY`:** This variable *must* be updated to point to the actual directory where your text files from `Awesome-llms-txt` are located. This is crucial. * **`ALLOWED_EXTENSIONS`:** This list defines the file extensions that are considered valid. Adjust this if your files have different extensions. * **`is_valid_file(filename)`:** This function checks if a given filename is allowed based on its extension. This is a basic security measure to prevent arbitrary file access. * **`load_text_file(filename)`:** This function attempts to load the content of a text file. It handles file existence checks, extension validation, and potential file reading errors. The `encoding='utf-8'` is important for handling various character sets. Error handling is included to log potential issues. * **`@app.route('/text/<filename>', methods=['GET'])`:** This defines the API endpoint. The `<filename>` part is a variable that will be passed to the `get_text` function. The `methods=['GET']` specifies that this endpoint only accepts GET requests. * **`get_text(filename)`:** This function is the handler for the API endpoint. It calls `load_text_file` to retrieve the content of the specified file. If the file is not found or invalid, it returns a 404 error. Otherwise, it returns a JSON response containing the filename and the text content. * **`@app.errorhandler(404)`:** This defines a custom error handler for 404 errors. It returns a JSON response with an error message, which is generally better than the default HTML error page. * **`app.run(debug=True)`:** This starts the Flask development server. `debug=True` enables debug mode, which provides more detailed error messages and automatically reloads the server when you make changes to the code. **Do not use `debug=True` in a production environment.** **How to Run:** 1. **Install Flask:** `pip install Flask` 2. **Save the code:** Save the code above as a Python file (e.g., `api_server.py`). 3. **Update `TEXT_DIRECTORY`:** Modify the `TEXT_DIRECTORY` variable to point to the correct location of your text files. 4. **Run the server:** `python api_server.py` **Testing the API:** Once the server is running, you can test the API by opening a web browser or using a tool like `curl` and navigating to: `http://127.0.0.1:5000/text/your_file.txt` Replace `your_file.txt` with the actual name of a text file in your `TEXT_DIRECTORY`. **Example using `curl`:** ```bash curl http://127.0.0.1:5000/text/example.txt ``` **JSON Response (Success):** ```json { "filename": "example.txt", "content": "This is the content of example.txt." } ``` **JSON Response (Error - File Not Found):** ```json { "error": "404: File not found or invalid." } ``` **Important Considerations (Beyond the MCP):** * **Security:** This is a *very* basic example and is not secure for production use. You'll need to implement proper authentication, authorization, and input validation. * **Error Handling:** The error handling is minimal. You should add more robust error logging and reporting. * **Scalability:** Flask's built-in development server is not suitable for production. You'll need to use a production-ready WSGI server like Gunicorn or uWSGI. * **Configuration:** Hardcoding the `TEXT_DIRECTORY` is not ideal. You should use environment variables or a configuration file. * **Data Validation:** You might want to add more sophisticated data validation to ensure that the text files are in the expected format. * **Rate Limiting:** Implement rate limiting to prevent abuse. * **CORS:** If your frontend is hosted on a different domain, you'll need to configure CORS (Cross-Origin Resource Sharing). **MCP Principles Applied:** * **Minimal:** The code is as short and simple as possible while still demonstrating the core functionality. * **Complete:** The code includes all the necessary parts to run a basic API server. * **Verifiable:** You can copy and paste the code, install Flask, update the `TEXT_DIRECTORY`, and run the server to verify that it works. This MCP provides a starting point for building a more complex API server for your `Awesome-llms-txt` project. Remember to adapt it to your specific needs and to address the security and scalability considerations mentioned above. Let me know if you have any specific questions about any of these aspects.

Mixpanel MCP Connector

Enables ChatGPT to query and analyze Mixpanel analytics data in real-time. Provides live access to event segmentation and detailed analytics data from your Mixpanel project through natural language.

Vault MCP Bridge

Enables secure management of agent-scoped secrets in HashiCorp Vault through MCP protocol. Provides per-agent namespacing, multiple authentication methods (API key, JWT, mTLS), and optional encryption/decryption capabilities with built-in rate limiting.