MCP Server Practice

by mybarefootstory

247 downloads
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Description

# MCP Server Practice This repository contains implementations of Model Context Protocol (MCP) servers for LinkedIn profile scraping and weather data retrieval. The MCP framework facilitates seamless integration and communication between AI services. ## Overview - **LinkedIn…

About

# MCP Server Practice This repository contains implementations of Model Context Protocol (MCP) servers for LinkedIn profile scraping and weather data retrieval. The MCP framework facilitates seamless integration and communication between AI services. ## Overview - **LinkedIn Profile Scraper**: Fetches LinkedIn profile…

Details

Author
mybarefootstory
Downloads
247
Categories
Other

- LinkedIn profile scraping via Fresh LinkedIn Data API
- Weather alerts and forecasts from NWS API
- Asynchronous HTTP requests using httpx
- Environment variable management with python-dotenv
- Implements MCP servers with FastMCP framework
- Stdio transport for tool invocation

Clone the repository, install dependencies (httpx, python-dotenv, mcp), and create a .env file with your RAPIDAPI_KEY. Run the desired server script; for LinkedIn, use the get_profile tool with a LinkedIn URL, and for weather, use get_alerts (by US state) or get_forecast (by latitude/longitude). Both servers communicate over stdio transport.

MCP Server Practice

This repository contains implementations of Model Context Protocol (MCP) servers for LinkedIn profile scraping and weather data retrieval. The MCP framework facilitates seamless integration and communication between AI services.

Overview

- LinkedIn Profile Scraper: Fetches LinkedIn profile data using the Fresh LinkedIn Profile Data API. - Weather Data Service: Retrieves weather alerts and forecasts using the National Weather Service (NWS) API.

Prerequisites

- Python 3.7+ - httpx for asynchronous HTTP requests - python-dotenv for environment variable management - mcp for MCP server implementation

Installation

1. Clone the repository: ``bash git clone https://github.com/mybarefootstory/MCP-Server-Practice-2.git cd MCP-Server-Practice-2 ` 2. Install dependencies: `bash pip install httpx python-dotenv mcp ` 3. Set up environment variables: - Create a .env file in the root directory. - Add your RapidAPI key: ` RAPIDAPI_KEY=your_rapidapi_key_here `

LinkedIn Profile Scraper

Description

Fetches LinkedIn profile data using the Fresh LinkedIn Profile Data API. The server is initialized with
FastMCP and listens for requests to retrieve profile information.

Code Snippet

`python from mcp.server.fastmcp import FastMCP import httpx import os from dotenv import load_dotenv load_dotenv() RAPIDAPI_KEY = os.getenv("RAPIDAPI_KEY") mcp = FastMCP("linkedin_profile_scraper") async def get_linkedin_data(linkedin_url: str) -> dict: # Fetch LinkedIn profile data ... @mcp.tool() async def get_profile(linkedin_url: str) -> str: # Get LinkedIn profile data ... if __name__ == "__main__": mcp.run(transport="stdio") `

Weather Data Service

Description

Retrieves weather alerts and forecasts using the NWS API. The server is initialized with
FastMCP and provides tools for fetching alerts and forecasts.

Code Snippet

`python from mcp.server.fastmcp import FastMCP import httpx mcp = FastMCP("weather") async def make_nws_request(url: str) -> dict: # Make a request to the NWS API ... @mcp.tool() async def get_alerts(state: str) -> str: # Get weather alerts for a US state ... @mcp.tool() async def get_forecast(latitude: float, longitude: float) -> str: # Get weather forecast for a location ... if __name__ == "__main__": mcp.run(transport='stdio') `

Usage

- LinkedIn Profile Scraper: Run the server and use the
get_profile tool to fetch LinkedIn data. - Weather Data Service: Run the server and use the get_alerts and get_forecast` tools to retrieve weather information.
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