Discover Awesome MCP Servers

MCP Chain of Draft Server 🧠

Espejo de

Browser Automation MCP Server

A Model Context Protocol (MCP) server that provides browser automation capabilities for Claude and other MCP-compatible AI assistants

🕹️ Welcome to the Minesweeper MCP Server

An MCP server for playing Minesweeper

Lamda

🤖 El framework RPA de Android más potente, la próxima generación de robots de automatización móvil.

AI Chat Desktop Applications

Electron-based desktop applications for various AI chat platforms.

Dida365 MCP Server

Servidor MCP para dida365.com

Redmine MCP Server

Mirror of

Google-Calendar-MCP-Server

Servidor MCP para la API de Google Calendar

A-MEM MCP Server

Memory Control Protocol (MCP) server for the Agentic Memory (A-MEM) system - a flexible, dynamic memory system for LLM agents

Covid Mcp Server

Here are a few options for an MCP (presumably meaning "Minimal, Complete, and Verifiable") server setup for fetching real-time COVID-19 data by country, along with considerations for each: **Option 1: Simple Python Flask API with a Single Data Source** This is the easiest to implement for a quick prototype. * **Language:** Python * **Framework:** Flask (lightweight web framework) * **Data Source:** Johns Hopkins University CSSE COVID-19 Data (widely used, updated frequently) * **Libraries:** `requests`, `pandas` ```python from flask import Flask, jsonify import pandas as pd import requests app = Flask(__name__) DATA_URL = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv" @app.route('/covid/country/<country_name>') def get_covid_data(country_name): """ Returns COVID-19 confirmed cases for a given country. """ try: df = pd.read_csv(DATA_URL) country_data = df[df['Country/Region'] == country_name] if country_data.empty: return jsonify({"error": "Country not found"}), 404 # Sum across provinces/states within the country country_data = country_data.groupby('Country/Region').sum() # Extract the time series data (dates as columns) data = country_data.iloc[:, 3:].to_dict(orient='records')[0] # Skip lat/long columns return jsonify({"country": country_name, "data": data}) except Exception as e: print(f"Error: {e}") return jsonify({"error": "Internal server error"}), 500 if __name__ == '__main__': app.run(debug=True) ``` **Explanation:** 1. **Imports:** Imports necessary libraries. 2. **`DATA_URL`:** Defines the URL for the Johns Hopkins data. 3. **`get_covid_data(country_name)`:** * Reads the CSV data into a Pandas DataFrame. * Filters the DataFrame to get data for the specified `country_name`. * Handles the case where the country is not found. * Groups by country to sum cases across provinces/states. * Extracts the time series data (dates as columns) into a dictionary. * Returns the data as a JSON response. 4. **Error Handling:** Includes basic error handling. 5. **`if __name__ == '__main__':`:** Starts the Flask development server. **To run this:** 1. Save the code as `app.py`. 2. Install dependencies: `pip install flask pandas requests` 3. Run: `python app.py` 4. Access the API: `http://127.0.0.1:5000/covid/country/US` (replace "US" with the country you want). **Advantages:** * Simple to implement. * Uses a well-known data source. * Easy to understand. **Disadvantages:** * Relies on a single data source. If that source goes down, your API is down. * Limited error handling. * No data caching. Each request re-downloads the entire CSV. * No data cleaning or validation. * Only provides confirmed cases. You might want deaths, recovered, etc. * The Johns Hopkins data format can change, breaking your code. **Option 2: More Robust with Caching and Multiple Data Sources (Conceptual)** This is a more production-ready approach. * **Language:** Python * **Framework:** Flask or FastAPI (FastAPI is generally preferred for APIs) * **Data Sources:** * Johns Hopkins CSSE * Worldometer * Our World in Data * **Libraries:** `requests`, `pandas`, `cachetools` (for caching) * **Database (Optional):** Consider a database (e.g., PostgreSQL, MySQL) for storing cleaned and aggregated data if you need more complex queries or historical data analysis. **Key Improvements:** * **Data Source Redundancy:** Fetch data from multiple sources and implement logic to handle discrepancies or missing data. This makes your API more resilient. * **Caching:** Use `cachetools` or a similar library to cache the data in memory. This significantly reduces the load on the data sources and speeds up responses. Implement a cache invalidation strategy (e.g., refresh the cache every hour). * **Data Cleaning and Validation:** Clean and validate the data before storing it or returning it. This includes handling missing values, correcting inconsistencies, and ensuring data types are correct. * **Error Handling:** Implement robust error handling and logging. * **Data Aggregation:** Pre-aggregate the data by country and date to improve query performance. * **API Documentation:** Use Swagger/OpenAPI (built into FastAPI) to automatically generate API documentation. * **Rate Limiting:** Implement rate limiting to prevent abuse of your API. **Conceptual Code Snippet (Caching):** ```python from flask import Flask, jsonify import pandas as pd import requests import cachetools import time app = Flask(__name__) DATA_URL = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv" # Cache configuration cache = cachetools.TTLCache(maxsize=100, ttl=3600) # Cache for 1 hour (3600 seconds) def fetch_covid_data_from_source(): """Fetches data from the data source and returns a Pandas DataFrame.""" try: df = pd.read_csv(DATA_URL) return df except Exception as e: print(f"Error fetching data: {e}") return None def get_covid_data_from_cache(): """Gets COVID-19 data from the cache, or fetches it if not in the cache.""" if 'covid_data' in cache: print("Fetching data from cache") return cache['covid_data'] else: print("Fetching data from source") data = fetch_covid_data_from_source() if data is not None: cache['covid_data'] = data return data else: return None @app.route('/covid/country/<country_name>') def get_covid_data(country_name): """ Returns COVID-19 confirmed cases for a given country. """ try: df = get_covid_data_from_cache() if df is None: return jsonify({"error": "Failed to fetch data"}), 500 country_data = df[df['Country/Region'] == country_name] if country_data.empty: return jsonify({"error": "Country not found"}), 404 # Sum across provinces/states within the country country_data = country_data.groupby('Country/Region').sum() # Extract the time series data (dates as columns) data = country_data.iloc[:, 3:].to_dict(orient='records')[0] # Skip lat/long columns return jsonify({"country": country_name, "data": data}) except Exception as e: print(f"Error: {e}") return jsonify({"error": "Internal server error"}), 500 if __name__ == '__main__': app.run(debug=True) ``` **Option 3: Using an Existing COVID-19 API** Instead of building your own, consider using a well-maintained existing API. This can save you a lot of time and effort. * **Example:** There are several public COVID-19 APIs available. Search for "public covid api" on Google. Be sure to check the terms of service and reliability of any API you choose. **Spanish Translation of Key Terms:** * **MCP Server:** Servidor MCP (Minimal, Completo y Verificable) * **Real-time data:** Datos en tiempo real * **Country:** País * **Confirmed cases:** Casos confirmados * **Deaths:** Muertes * **Recovered:** Recuperados * **Data source:** Fuente de datos * **Caching:** Almacenamiento en caché * **API:** API (Interfaz de Programación de Aplicaciones) * **Error handling:** Manejo de errores * **Rate limiting:** Limitación de velocidad * **Database:** Base de datos **Recommendations:** * Start with Option 1 to get a basic API working. * If you need more reliability and features, move to Option 2. * Consider Option 3 if you want to avoid building your own API. * Always prioritize data quality and reliability. * Be aware of the terms of service of any data sources or APIs you use. * Implement proper error handling and logging. * Consider using a database for storing and querying data. * Use a framework like Flask or FastAPI to simplify API development. * Deploy your API to a cloud platform like AWS, Google Cloud, or Azure for scalability and reliability. Remember to adapt the code and architecture to your specific needs and requirements. Good luck!

SeaTunnel MCP Server

Un servidor de Protocolo de Contexto de Modelo que permite la interacción con Apache SeaTunnel a través de interfaces LLM, permitiendo a los usuarios gestionar trabajos, monitorizar información del sistema y configurar conexiones a través del lenguaje natural.

InsightFlow

InsightFlow: un servidor de panel de control de análisis en tiempo real con una arquitectura MCP (Protocolo de Control de Mensajes) que se integra con servicios de IA como Claude o Cursor. Esta solución permite el análisis de datos en tiempo real con capacidades de consulta en lenguaje natural.

AWS Resources MCP Server

Espejo de

SSH Tools MCP

SSH tools and utilities for MCP servers

MCP Template

A barebones MCP server implementation in Swift using loopwork-ai's mcp-swift-sdk.

Shopify MCP Server for Claude Desktop

Un servidor MCP de Shopify

Cloud Foundry MCP Server

Servidor MCP de Cloud Foundry

Exa MCP Server 🔍

Espejo de

Jupiter MCP Server

Un servidor MCP para ejecutar intercambios de tokens en la blockchain de Solana utilizando la API Ultra de Jupiter, que permite a los usuarios obtener órdenes de intercambio óptimas y ejecutar transacciones con control de deslizamiento.

PubChem MCP Server

Permite que los modelos de lenguaje grandes consulten correctamente bases de datos moleculares y generen archivos de estructura.

fal.ai MCP Server

A Model Context Protocol (MCP) server for interacting with fal.ai models and services.

s-GitHubTestRepo

created from MCP server demo

Exa MCP Server

AI-powered code search MCP server using Exa API for intelligent code search and retrieval in AI assistants

attio-mcp-server

Mirror of

SSH MCP Server

Una implementación de servidor del Protocolo de Contexto de Modelo que permite la ejecución segura de comandos remotos a través de SSH, con funciones para administrar y utilizar credenciales SSH.