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Mcp Server: Lightning Performance & Seamless Scalability - MCP Implementation

Mcp Server: Lightning Performance & Seamless Scalability

Unleash lightning-fast performance and seamless scalability with Mcp Server—designed for your business’s growth, innovation, and 24/7 reliability. Elevate operations effortlessly.

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About Mcp Server

What is Mcp Server: Lightning Performance & Seamless Scalability?

Mcp Server is a high-performance API gateway engineered for real-time data integration. Built on a modular architecture, it delivers sub-second response times while dynamically scaling to handle fluctuating workloads. This robust solution aggregates financial, meteorological, and news data streams into a unified interface, offering developers a streamlined way to embed critical information into applications without compromising speed or reliability.

How to Use Mcp Server: Lightning Performance & Seamless Scalability?

Implementing Mcp Server requires minimal configuration. Begin by cloning the repository and setting up a Python virtual environment. Dependencies are managed via pip, ensuring frictionless deployments. Once operational, access three core endpoints using standard HTTP requests: /exchange_rate for currency conversions, /weather for localized forecasts, and /news for curated news digests. The included Python client demonstrates best practices for asynchronous data fetching.

Mcp Server Features

Key Features of Mcp Server: Lightning Performance & Seamless Scalability?

  • Currency Oracle: Millisecond-accurate USD/RUB exchange rates fetched from reliable financial APIs
  • Hyperlocal Weather: Granular forecasts for 5000+ global cities with adaptive geolocation handling
  • News Aggregation: Curated top stories from vetted sources, updated hourly

Use Cases of Mcp Server: Lightning Performance & Seamless Scalability?

Mcp Server FAQ

FAQ from Mcp Server: Lightning Performance & Seamless Scalability?

Does the server support horizontal scaling?

Yes - built using asynchronous frameworks, it can be easily deployed across multiple nodes with load balancers for enterprise-grade availability

What caching mechanisms are used?

Data freshness policies with LRU caching for frequently accessed resources ensure optimal performance without compromising accuracy

How is error handling managed?

Comprehensive retry logic with exponential backoff and detailed error codes at both API and application layers

Content

MCP (Multi-Functional Command Processor) сервер предоставляет API для получения текущего курса доллара, прогноза погоды в заданном городе и сводки новостей за последнюю неделю.

Возможности

  • Курс доллара: Получение текущего курса доллара к рублю.
  • Прогноз погоды: Получение текущей погоды в указанном городе.
  • Сводка новостей: Получение новостей за последнюю неделю.

Установка и запуск

Предварительные требования

  • Python 3.7 или выше
  • Установленный pip

Установка

  1. Клонируйте репозиторий:

    git clone https://github.com/VenGr0/mcp-server.git

cd mcp-server
  1. Создайте виртуальное окружение и активируйте его:

    python -m venv venv

source venv/bin/activate  # Для Linux/MacOS
venv\Scripts\activate     # Для Windows
  1. Установите зависимости:

    pip install -r requirements.txt

Запуск сервера

Запустите сервер:

python server.py

Сервер будет доступен по адресу http://127.0.0.1:5000.

Запуск тестов

Для запуска тестов выполните:

pytest tests/test_server.py

Использование API

Получение курса доллара

Запрос:

GET /exchange_rate

Ответ:

{
  "usd_rate": 75.50
}

Получение прогноза погоды

Запрос:

GET /weather?city=Moscow

Ответ:

{
  "weather": "clear sky",
  "temperature": 20
}

Получение сводки новостей

Запрос:

GET /news

Ответ:

{
  "articles": [
    {
      "title": "Новость 1",
      "description": "Описание новости 1",
      "url": "https://example.com/news1"
    },
    {
      "title": "Новость 2",
      "description": "Описание новости 2",
      "url": "https://example.com/news2"
    }
  ]
}

Подключение клиента

Пример клиента на Python:

import requests

BASE_URL = 'http://127.0.0.1:5000'

def get_exchange_rate():
    response = requests.get(f'{BASE_URL}/exchange_rate')
    return response.json()

def get_weather(city):
    response = requests.get(f'{BASE_URL}/weather?city={city}')
    return response.json()

def get_news():
    response = requests.get(f'{BASE_URL}/news')
    return response.json()

if __name__ == '__main__':
    print(get_exchange_rate())
    print(get_weather('Moscow'))
    print(get_news())

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