mcp_weather_scraper
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MCP Weather Scraper is an experimental project using Model Context Protocol (MCP) with OpenAI and FastAPI to fetch real-time weather data.
The MCP Weather Scraper is a cutting-edge implementation of the Model Context Protocol (MCP) designed to leverage lightweight language models (LLMs) and FastAPI for retrieving and structuring real-time weather information from the web. By integrating OpenAI's LLMs, such as gpt-3.5-turbo, the project aims to demonstrate how LLMs can function as intelligent agents capable of interacting with tools to extract and reason over unstructured web data. The server is MCP-compliant and utilizes FastAPI to provide weather information as a callable MCP tool. The project also features a Streamlit app frontend for user interaction and employs `selectolax` for efficient HTML parsing. Additionally, response caching is implemented using `functools.lru_cache` to enhance performance.
Features
- MCP-compliant server with weather scraping via browser search
- Integration with OpenAI LLM (e.g., gpt-3.5-turbo)
- FastAPI server provides weather info as callable MCP tool
- Automatic HTML parsing using `selectolax` for performance
- Response caching using `functools.lru_cache`
