Discover Awesome MCP Servers

Google Cloud Logging MCP Server

Enables AI assistants to query, search, and analyze logs across Google Cloud Platform projects. It supports advanced filtering by severity or resource type and provides detailed log entry retrieval and project listing capabilities.

Azure Analysis Services MCP Server by CData

Azure Analysis Services MCP Server by CData

Trade Agent

Trade Agent

mcp-server-example

Aqui está um exemplo simples de um servidor MCP (Minecraft Coder Pack) para ajudar a entender melhor: **Em Inglês (Original):** "Let's say you want to change how creepers explode. Without MCP, you'd have to look through obfuscated code like `a.b.c(d,e,f)`. With MCP, you can see something like `EntityCreeper.explode(damageMultiplier)`. Much easier to understand and modify!" **Em Português (Tradução):** "Digamos que você queira mudar como os creepers explodem. Sem o MCP, você teria que procurar em código ofuscado como `a.b.c(d,e,f)`. Com o MCP, você pode ver algo como `EntityCreeper.explode(damageMultiplier)`. Muito mais fácil de entender e modificar!" **Explicação:** * **Código Ofuscado:** O código original do Minecraft é "ofuscado", o que significa que os nomes das variáveis e funções são alterados para dificultar a leitura e a compreensão. Isso é feito para proteger a propriedade intelectual. * **MCP (Minecraft Coder Pack):** O MCP desofusca o código, substituindo os nomes obscuros por nomes mais legíveis e significativos. * **Exemplo:** O exemplo mostra como o MCP torna o código mais fácil de entender. Em vez de ter que lidar com nomes de variáveis e funções sem sentido, você pode ver nomes que descrevem o que o código realmente faz. Isso facilita muito a modificação do jogo. Em resumo, o MCP é uma ferramenta que torna o código do Minecraft mais legível e compreensível, facilitando a criação de mods.

MyPos MCP

A Model-Context-Protocol server that connects to MySQL or PostgreSQL databases, allowing users to list tables, execute SELECT queries, and retrieve column information.

prometheus-mcp

Provides seamless integration between AI assistants and Prometheus, enabling natural language interactions with your monitoring infrastructure. This server allows for effortless querying, discovery, and analysis of metrics.

Shrike Security MCP Server

Protects AI agents from threats like prompt injection, jailbreaks, and SQL injection through a multi-layer scanning pipeline. It also enables PII redaction and rehydration to ensure data privacy during LLM interactions.

mcp-nordic

Provides access to a suite of Nordic data tools covering Danish business records, addresses, weather, and energy prices, alongside Norwegian and Finnish company information. This unified server enables users to query public APIs for regional data across Denmark, Norway, and Finland without requiring individual API keys.

Dynamics 365 MCP Server

Um servidor de Protocolo de Contexto de Modelo que permite a interação com o Microsoft Dynamics 365 CRM a partir do Claude Desktop, permitindo que os usuários recuperem, criem e atualizem dados do CRM por meio de linguagem natural.

Markdown + Mermaid to PDF MCP Server

Converts Markdown files and standalone Mermaid diagrams into high-quality PDF or PNG documents using Puppeteer and SVG rendering. It supports custom CSS styling and provides tools for professional document generation from markdown-based content.

Soduku Solver MCP Server

Um servidor MCP simples que implementa um sistema de armazenamento de notas com uma ferramenta para adicionar notas e um prompt para resumir as notas armazenadas.

Amazon CloudWatch Logs MCP Server

Um servidor de Protocolo de Contexto de Modelo (MCP) que permite que assistentes de IA interajam com o Amazon CloudWatch Logs através de uma interface padronizada usando o AWS SDK.

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.

Document Q&A Server

Permite consultar documentos através de um backend Langflow usando perguntas em linguagem natural, fornecendo uma interface para interagir com fluxos de Q\&A de documentos Langflow.

Semantic Search MCP Server

Provides hybrid semantic and keyword code search for Claude Code using BM25 and vector retrieval. It enables indexing and searching local codebases with language-aware chunking and local embeddings.

Office Word MCP Server

Enables AI assistants to create, read, and manipulate Microsoft Word documents with comprehensive formatting, table creation, content management, and document protection capabilities. Supports advanced operations like merging documents, PDF conversion, and rich text formatting through a standardized interface.

GoWeb3 Data

GoWeb3 Data MCP Server provides events and news curated by GoWeb3.fyi

dev-prompt-mcp

An MCP server that registers customizable prompt templates as tools for natural language interaction. It supports hot-reloading and easy extension via YAML or JSON files to automate tasks like code review, refactoring, and content summarization.

MCP Ky2Help

An MCP server that enables interaction with Ky2Help, providing tools to retrieve and manage Ky2Help changes. It leverages the Model Context Protocol to bridge language models with Ky2Help management systems.

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.

ATEM MCP Server

Enables natural language control of Blackmagic ATEM video switchers via the Model Context Protocol. It allows users to manage camera switching, transitions, audio mixing, macros, and streaming operations through AI assistants.

Inbound Email MCP Server

Enables AI assistants to interact with the Inbound Email API to manage domains, endpoints, and email communications. Users can send or schedule emails, manage webhooks, and retrieve email threads through natural language commands.

MCP fal.ai Image Server

Enables AI-powered image generation from text prompts using fal.ai models directly within IDEs. Supports multiple models, customizable parameters, and saves generated images locally with accessible file paths.

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!

PokeMCP

Enables AI assistants to play Pokemon Fire Red through the mGBA emulator by providing tools for button inputs and screenshots. It allows for direct reading of real-time game state from RAM, including party information, player location, and battle status.

Pocket Assistant MCP Server

Enables AI assistants to save, retrieve, and manage research content using ChromaDB vector storage with semantic search, topic organization, and automatic deduplication powered by OpenAI embeddings.

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.