Discover Awesome MCP Servers

PostgreSQL MCP Server

Um servidor de Protocolo de Contexto de Modelo que permite a interação com bancos de dados PostgreSQL para listar tabelas, recuperar esquemas e executar consultas SQL somente leitura.

EduChain MCP Server

Enables AI-powered educational content generation, including multiple-choice questions and lesson plans, through a modular command platform.

scholar-mcp

A FastMCP server for the scholarly citation landscape that enables LLMs to search, cross-reference, and retrieve prior art across papers, patents, books, and standards via multiple APIs.

@alpha-arcade/mcp

MCP server for Alpha Arcade prediction markets on Algorand. Enables AI agents to browse markets, fetch orderbooks, place orders, manage positions, and trade on-chain prediction markets.

mcp-on-fire

Um repositório para MCP no Fire com um README incrível sobre o servidor GitHub MCP.

neuro-cube-brain

Enables AI assistants to read real-time focus state from any BrainFlow-supported EEG headset.

hotmart-mcp

An MCP server for the Hotmart digital products platform that enables management of sales, subscriptions, and products. It provides tools for listing users, processing refunds, and monitoring commissions.

JauMemory MCP Server

Provides persistent memory capabilities for AI assistants, enabling storage, recall, and analysis of information across conversations with intelligent memory management.

AtlassianJira MCP Integration Server

Enables Jira integration with dynamic configuration, time logging, task creation, issue updates, and bulk imports through natural language.

whed-tools

An MCP-native pipeline for collecting structured intelligence on higher education institutions using the WHED schema, enabling scraping, extraction, validation, and saving of profiles.

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.

Qwen MCP Tool

Enables AI assistants to leverage Qwen's code analysis capabilities with large context windows, supporting file/directory analysis, sandbox execution, and multiple approval modes for safe code operations.

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.

Materio MCP Server

Enables AI assistants to search and read Materio's educational PDF resources directly, without manual file uploads.

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!

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.

Mcp Server

Enables dependency, security, and coding convention checks for Python and Java projects via MCP tools. Supports local paths or Git URLs.

GDA-MCP-Server

Enables Android APK static analysis in Cursor/Claude via GDA CLI Server, supporting reconnaissance, attack surface scanning, and code decompilation through natural language.

MCP Server with Docker

A project that integrates Model Control Protocol with OpenAI's API, allowing OpenAI to access and utilize tools exposed by a dockerized MCP server.

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.

secret-scanner

Enables scanning diffs or code blobs for leaked secrets, returning a verdict with severity and masked findings, all processed locally with no data sent externally.

dejared-mcp

A Model Context Protocol (MCP) server for exploring, analyzing, and decompiling Java JAR files.

airflow-unfactor

An MCP server that converts Apache Airflow DAGs into Prefect flows. It provides tools to read DAGs, lookup translation knowledge, validate code, search Prefect docs, scaffold projects, deploy, and generate migration reports.

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.

GoWeb3 Data

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

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.

CodeRecoder MCP

An intelligent code version management system based on the MCP protocol, providing AI-enhanced code snapshots, project versioning, and smart change detection for AI-assisted programming.

mcp-witness

Cryptographic proof of every AI decision. An immutable, verifiable audit trail MCP server.