Discover Awesome MCP Servers

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.

spring-boot-mcp-server-hello-world

Hello World MCP Server

Personas-MCP-Server

Okay, I understand. You're looking for a translation of "Model Context Protocol server implementation for my AI personas" into Spanish. Here are a few options, with slightly different nuances, depending on the specific context you want to emphasize: **Option 1 (Most Direct):** * **Implementación del servidor del Protocolo de Contexto del Modelo para mis personajes de IA** * This is the most literal and straightforward translation. It's suitable if you want to be very precise and technical. **Option 2 (Slightly More Natural):** * **Implementación de un servidor del Protocolo de Contexto del Modelo para mis personajes de IA** * Adding "un" (a/an) before "servidor" can make it sound a bit more natural in Spanish. **Option 3 (Focus on Building/Creating):** * **Implementación de un servidor para el Protocolo de Contexto del Modelo para mis personajes de IA** * This option is similar to option 2, but it removes the "de" after "Implementación" which can be more natural in some contexts. **Option 4 (Emphasis on Functionality):** * **Implementación de un servidor que implementa el Protocolo de Contexto del Modelo para mis personajes de IA** * This translates to "Implementation of a server that implements the Model Context Protocol for my AI personas." It emphasizes that the server *performs* the protocol. **Which one is best?** * **If you need the most direct and technical translation, use Option 1.** * **If you want a slightly more natural sound, use Option 2 or 3.** * **If you want to emphasize the server's role in *implementing* the protocol, use Option 4.** In most cases, **Option 2 or 3** will be the most appropriate and sound the most natural.

s-GitHubTestRepo

created from MCP server demo

MCP Test Repository

Ett testrepository skapat via GitHub MCP-server

mcp-server-example

A learning repository for Model Context Protocol's server-side via Python.

mcp-unlock-pdf

MCP server to give client the ability read protected (or un-unprotected) PDF

CheerLights MCP Server

Servidor MCP que permite que las herramientas de IA interactúen con la API de CheerLights.

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.

Columbia MCP Servers

Espejo de

MCP Memos

A memo tool based on MCP protocol that helps developers quickly save and retrieve text information without interrupting their workflow.

🔐 get-mcp-keys

A lightweight utility that securely loads API keys for Cursor MCP servers from your home directory, preventing accidental exposure of secrets in repositories. Keep your credentials safe while maintaining seamless integration with AI coding assistants.

WhatsApp MCP Server

WhatsAPP MCP Server

Manifold Markets MCP Server

Espejo de

Solana MCP Server

Espejo de

mcp-email-server

Espejo de

yapi-mcp-server

Model Context Protocol (MCP) server for Yapi.

Meta MCP Server

Mirror of

MCP Weather Server

Here are a few ways to translate "A weather server application for MCP" into Spanish, depending on the specific context and desired nuance: **Option 1 (Most straightforward):** * **Una aplicación de servidor meteorológico para MCP.** * This is a direct translation and is generally suitable. **Option 2 (More descriptive):** * **Una aplicación de servidor del tiempo para MCP.** * Using "del tiempo" is a common way to refer to weather in Spanish. **Option 3 (If "MCP" is a specific platform or system):** * **Una aplicación de servidor meteorológico para la plataforma MCP.** (or "el sistema MCP") * This clarifies that "MCP" is a platform or system. **Option 4 (More formal):** * **Una aplicación de servidor de información meteorológica para MCP.** * This is a more formal way of saying "weather information server application." **Which option is best depends on the context:** * If "MCP" is well-understood, Option 1 or 2 is fine. * If you need to be more explicit about "MCP" being a platform, use Option 3. * If you need a more formal tone, use Option 4. Therefore, I recommend **"Una aplicación de servidor meteorológico para MCP."** as a good general translation.

Bitcoin MCP Server

Aquí tienes una demostración de un servidor MCP de Spring Boot/IA que rastrea los precios de Bitcoin utilizando las APIs de CoinGecko (api.coingecko.com/api/v3).

DeepRe - AI駆動の深い調査レポート生成ツール

Here are a few possible translations, depending on the context: * **Local MCP server: Perplejidad** (This is a direct translation, suitable if you're talking about the perplexity of a local MCP server.) * **Servidor MCP local: Perplejidad** (This is the same as above, just with the order switched to be more natural in Spanish.) * **Perplejidad del servidor MCP local** (This emphasizes that the perplexity *belongs to* the local MCP server.) Without more context, it's difficult to choose the absolute best translation. However, any of these options should be understandable.

MCP Server

Server implementation for MCP