Discover Awesome MCP Servers

Foundry MCP Project

Um servidor de Protocolo de Contexto de Modelo que permite a interação com ferramentas Foundry por meio de linguagem natural, permitindo que os usuários criem projetos, construam contratos, executem testes e gerenciem ambientes de desenvolvimento Ethereum.

FL Studio MCP

Um servidor MCP que conecta o Claude ao FL Studio, permitindo que a IA componha música, controle instrumentos e grave melodias, acordes e baterias ao vivo no piano roll.

Bootstrap MCP Server

A minimal MCP server template with hot reload support that helps developers quickly set up and build Model Context Protocol servers with example tool implementations.

Misc MCP Server

A collection of MCP servers that enable users to interact with AdGuard Home through natural language commands, allowing management of DNS records and filtering rules.

MCP Server - HTTP with SSE

Servidor MCP - HTTP com SSE - Para servidores remotos, inicie um servidor web que inclua um endpoint Server-Sent Events (SSE) para enviar atualizações para os clientes, juntamente com um endpoint separado para receber mensagens enviadas pelo cliente.

BnF API Server

Um servidor Modelo-Cliente-Protocolo que permite aos usuários pesquisar a biblioteca digital Gallica da Biblioteca Nacional da França e gerar relatórios de pesquisa sequenciais estruturados com citações formatadas e imagens relevantes.

Second Brain OS MCP Server

Facilitates integration with Claude Desktop to run AI agents and execute purchased actions without code, leveraging the Model Context Protocol framework.

Task Orchestration

Task Orchestration

esp-component-registry-mcp

Servidor MCP para Registro de Componentes ESP

MCP MYSQL SERVER

mysql数据库连接，改 f4ww4z/mcp-mysql-server中的连接配置支持cherry studio

Model Context Protocol (MCP) Server

A unified API server that enables interaction with multiple AI model providers like Anthropic and OpenAI through a consistent interface, supporting chat completions, tool calling, and context handling.

Xano MCP Server for Smithery

Um servidor de Protocolo de Contexto de Modelo que permite que a IA Claude interaja com bancos de dados Xano, fornecendo operações abrangentes de banco de dados, gerenciamento de arquivos e rastreamento do histórico de solicitações por meio de uma interface padronizada.

Perplexity AI Server

Fornece uma maneira padronizada de integrar recursos da Perplexity AI, como chat, pesquisa e acesso à documentação, em sistemas baseados em MCP.

Excel MCP Server

Oferece capacidades de manipulação de arquivos Excel sem exigir a instalação do Microsoft Excel, permitindo a criação de pastas de trabalho, manipulação de dados, formatação e recursos avançados do Excel.

Browser Agent MCP

Browser Agent MCP

UniversalDeFi AI

Um serviço de backend que executa transações em múltiplas blockchains, permitindo que usuários gerenciem carteiras, transfiram tokens e interajam com contratos inteligentes usando a estrutura do Protocolo de Contexto de Modelo.

ClickSend MCP Server

Servidor MCP para Mensagens usando ClickSend

Real Estate MCP Server

A comprehensive Model Context Protocol server for real estate data management that provides tools and resources for property listings, agent management, market analysis, client relationships, and area intelligence.

Canvas MCP Server

A Model Context Protocol server that enables AI assistants to interact with Canvas LMS through its API, providing tools for managing courses, users, content, assessments, and quizzes.

kaggle-mcp

Okay, I understand. I can act as a helpful assistant that translates text from English to Portuguese and also describes how I can interact with the Kaggle API to provide tools for searching and downloading datasets, and a prompt for generating EDA notebooks. Here's a breakdown of how I can do that: **1. Translation (English to Portuguese):** * I can translate any English text you provide into Portuguese. Just give me the English text, and I'll provide the Portuguese translation. For example: * **You:** "Hello, how are you?" * **Me:** "Olá, como você está?" **2. Kaggle API Interaction (Description):** While I can't directly execute code or interact with external APIs like the Kaggle API, I can describe how you would use it and provide the necessary code snippets and explanations. Here's how it would work: * **Kaggle API Setup:** You would need to install the Kaggle API client on your system (usually via `pip install kaggle`). You'd also need to authenticate by downloading your `kaggle.json` file from your Kaggle account settings and placing it in the appropriate directory (usually `~/.kaggle/`). * **Searching for Datasets:** You can use the Kaggle API to search for datasets based on keywords. Here's an example of how you would do it in Python: ```python import os os.environ['KAGGLE_CONFIG_DIR'] = '/content' # or wherever your kaggle.json is from kaggle.api.kaggle_api_extended import KaggleApi api = KaggleApi() api.authenticate() search_term = "machine learning" # Example search term datasets = api.dataset_list(search=search_term) for dataset in datasets: print(f"Dataset: {dataset.title}") print(f"URL: https://www.kaggle.com/datasets/{dataset.ref}") print(f"Description: {dataset.description}") print("-" * 20) ``` * **Explanation:** * The code imports the necessary libraries. * `api.authenticate()` authenticates you with the Kaggle API using your credentials. * `api.dataset_list(search=search_term)` searches for datasets matching the `search_term`. * The code then iterates through the results and prints the dataset title, URL, and description. * **Downloading Datasets:** Once you've found a dataset you want to download, you can use the Kaggle API to download it. Here's an example: ```python import os os.environ['KAGGLE_CONFIG_DIR'] = '/content' # or wherever your kaggle.json is from kaggle.api.kaggle_api_extended import KaggleApi api = KaggleApi() api.authenticate() dataset_name = "username/dataset-name" # Replace with the actual dataset name (e.g., "uciml/iris") download_path = "./data" # The directory where you want to download the dataset api.dataset_download_files(dataset_name, path=download_path, unzip=True) print(f"Dataset '{dataset_name}' downloaded to '{download_path}'") ``` * **Explanation:** * `dataset_name` should be replaced with the actual name of the dataset (found in the dataset's URL on Kaggle). * `download_path` specifies the directory where the dataset will be downloaded. * `api.dataset_download_files()` downloads the dataset and, if `unzip=True`, automatically unzips it. **3. Prompt for Generating EDA Notebooks:** I can provide a prompt that you can use with a large language model (LLM) like GPT-3 or similar to generate an EDA (Exploratory Data Analysis) notebook. The prompt would include instructions on what kind of analysis to perform. Here's an example prompt: ``` You are an expert data scientist. Generate a Python notebook using Pandas, Matplotlib, and Seaborn to perform exploratory data analysis (EDA) on the [DATASET_NAME] dataset. The dataset is located at [DATASET_PATH]. The notebook should include the following sections: 1. **Introduction:** A brief overview of the dataset and the goals of the EDA. 2. **Data Loading and Inspection:** * Load the dataset into a Pandas DataFrame. * Display the first few rows of the DataFrame. * Print the shape of the DataFrame. * Print the data types of each column. * Check for missing values and handle them appropriately (e.g., imputation or removal). 3. **Descriptive Statistics:** * Calculate and display descriptive statistics for numerical columns (mean, median, standard deviation, min, max, etc.). * Calculate and display value counts for categorical columns. 4. **Data Visualization:** * Create histograms for numerical columns to visualize their distributions. * Create box plots for numerical columns to identify outliers. * Create bar charts for categorical columns to visualize their frequencies. * Create scatter plots to explore relationships between numerical variables. * Create heatmaps to visualize correlations between numerical variables. 5. **Insights and Conclusions:** Summarize the key findings from the EDA and draw conclusions about the dataset. Be sure to include clear and concise comments throughout the notebook to explain each step. Use appropriate visualizations to effectively communicate the data insights. Assume the dataset is a CSV file. Replace [DATASET_NAME] with the actual name of the dataset and [DATASET_PATH] with the actual path to the dataset file. ``` * **Explanation:** * This prompt tells the LLM to act as a data scientist and generate a Python notebook. * It specifies the libraries to use (Pandas, Matplotlib, Seaborn). * It outlines the sections that the notebook should include, covering data loading, inspection, descriptive statistics, and data visualization. * It provides specific instructions for each section, such as creating histograms, box plots, and scatter plots. * It emphasizes the importance of clear comments and effective visualizations. * It includes placeholders for the dataset name and path, which you would need to replace with the actual values. **How to Use Me:** 1. **Translation:** Provide me with the English text you want to translate to Portuguese. 2. **Kaggle API:** Ask me how to perform a specific task with the Kaggle API (e.g., "How do I search for datasets about image classification?"). I'll provide the code snippet and explanation. Remember that you'll need to execute the code yourself. 3. **EDA Notebook Generation:** Ask me to generate a prompt for creating an EDA notebook. I'll provide a prompt like the one above, which you can then use with an LLM. Let me know what you'd like me to do first!

MCP Agent Social Media Server

Provides social media functionality for AI agents, enabling them to login with unique handles, read filtered posts, and create posts or replies within team-based discussions.

DBeaver MCP Server

A Model Context Protocol server that enables AI assistants to access and query 200+ database types through existing DBeaver connections without additional configuration.

Bunq MCP

A Model Context Protocol server for Bunq that enables interaction with the Bunq banking API through OAuth integration.

mcp2tcp

Uma ponte que conecta hardware físico com grandes modelos de linguagem de IA através do Protocolo de Contexto de Modelo (MCP), permitindo o controle de dispositivos TCP por meio de linguagem natural.

MCP Server - Archi Track

Servidor MCP para pesquisar em uma API externa de uma aplicação web.

Dify Plugin Tools

Um plugin que permite ao Dify conectar-se a múltiplos servidores MCP (Model Control Protocol) usando HTTP com transporte Server-Sent Events, suportando configurações personalizadas para URLs, cabeçalhos e tempos limite.

Dokploy MCP Server

Exposes Dokploy functionalities as tools consumable via the Model Context Protocol, allowing AI models and other applications to programmatically manage projects and applications on a Dokploy server.

Linear MCP Server

Uma implementação em Deno que permite que modelos de IA interajam com a funcionalidade de rastreamento de issues do Linear, suportando a busca de issues, leitura de detalhes, atualização e adição de comentários.

Scrapybara MCP

Um servidor de Protocolo de Contexto de Modelo que permite que clientes de IA interajam com desktops virtuais Ubuntu, permitindo que eles naveguem na web, executem código e controlem instâncias através de ações de mouse/teclado e comandos bash.