About MCP Server Practice
MCP Server Practice is a Python repository with MCP servers for retrieving LinkedIn profile data and weather information. It provides separate tools for profile lookup, weather alerts, and forecasts.
Key features
- 01LinkedIn profile retrieval
- 02Weather alerts
- 03Weather forecasts
- 04Async HTTP requests
Use cases
- 01Retrieve LinkedIn profile information
- 02Check weather alerts
- 03Fetch weather forecasts
MCP support
It implements MCP servers that expose LinkedIn profile retrieval and weather data tools to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP 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+
httpxfor asynchronous HTTP requestspython-dotenvfor environment variable managementmcpfor MCP server implementation
Installation
-
Clone the repository:
git clone https://github.com/mybarefootstory/MCP-Server-Practice-2.git cd MCP-Server-Practice-2 -
Install dependencies:
pip install httpx python-dotenv mcp -
Set up environment variables:
- Create a
.envfile in the root directory. - Add your RapidAPI key:
RAPIDAPI_KEY=your_rapidapi_key_here
- Create a
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
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
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_profiletool to fetch LinkedIn data. - Weather Data Service: Run the server and use the
get_alertsandget_forecasttools to retrieve weather information.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.