What external APIs does MCP Server Practice use?
It uses the Fresh LinkedIn Profile Data API (via RapidAPI) for LinkedIn scraping and the public National Weather Service (NWS) API for weather data.
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MCP Server Practice is a repository containing two Model Context Protocol (MCP) server implementations: one for scraping LinkedIn profiles using the Fresh LinkedIn Profile Data API (via RapidAPI), and another for retrieving weather alerts and forecasts from the National Weather Service (NWS) API. It is intended for developers integrating AI services with LinkedIn profile data or real-time weather information.
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.
It uses the Fresh LinkedIn Profile Data API (via RapidAPI) for LinkedIn scraping and the public National Weather Service (NWS) API for weather data.
Python 3.7+, the libraries `httpx`, `python-dotenv`, and `mcp`, plus a valid RapidAPI key stored in a `.env` file.
Create a `.env` file in the project root and add `RAPIDAPI_KEY=your_key_here`. The key is required only for the LinkedIn scraper; the NWS weather service is free and does not need authentication.
Both servers run with `transport="stdio"`, as shown in the `mcp.run()` calls. This is the standard transport for MCP tools communicating over standard input/output.
The LinkedIn scraper depends on the Fresh LinkedIn Profile Data API, which is a third‑party, likely paid service. The weather service is limited to US data (alerts by state, forecasts by coordinates) and requires an internet connection for NWS API calls.
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