MCP Weather Scraper

An experimental weather information scraping project based on the MCP protocol, using OpenAI's lightweight LLM and FastAPI to obtain and structure real-time weather data from open web sources, exploring the ability of LLM as an intelligent agent to process unstructured web data.

Research and data Developer tools #Weather scraping #MCP protocol #LLM agent #Data parsing .Python

rating : 2 points

downloads : 8

update time : 2025-04-29

What is MCP Weather Scraper?

This is an experimental weather information acquisition service that uses artificial intelligence technology to automatically search, extract, and structure weather data from open web resources. The service is built based on the Model Context Protocol (MCP) standard and can be seamlessly integrated with various AI systems.

How to use MCP Weather Scraper?

You just need to provide a location name, and the service will automatically search and return the weather information of that place, including temperature, humidity, air quality, etc. It can be used through API calls or the Streamlit web interface.

Applicable scenarios

Suitable for scenarios such as application development that requires real-time weather data, weather query functions of intelligent assistants, and travel planning tools. Especially suitable for projects that require structured weather data rather than simple forecasts.

Main features

Intelligent weather searchUse AI technology to automatically search and extract weather information from the web without the need to know specific weather websites in advance.

Structured data outputConvert unstructured web weather information into standardized JSON format data.

Performance optimizationUse a high-speed HTML parser and cache mechanism to ensure fast response.

Usage statisticsProvide detailed token usage statistics to help understand AI processing costs.

Advantages and limitations

Advantages

No need for a fixed weather API, can obtain the latest data from multiple sources

Automatically process unstructured data and output in a standardized format

Built-in cache mechanism to improve performance

Provide detailed usage statistics

Limitations

Depends on web search results, and the accuracy may be affected by the search content

Using the OpenAI service will incur corresponding fees

Currently only supports English location queries

How to use

Install dependencies

Make sure Python 3.9+ is installed, and then run the command to install the required packages.

Set the API key

Add your OpenAI API key to the.env file.

Start the service

Run the following command to start the weather query service.

Query the weather

Query the weather information of a specific location through the API or web interface.

Usage examples

Travel planningWhen developing a travel planning application, integrate the weather query function to help users understand the weather conditions at the destination.

Smart homeThe smart home system automatically adjusts the indoor temperature according to the outdoor weather.

Frequently Asked Questions

Is this service free?

What is the data update frequency?

Which locations are supported for querying?

How to reduce token usage?

Related resources

Model Context Protocol official website

Official documentation and specifications of the MCP protocol

GitHub repository

Project source code and issue tracking

OpenAI API documentation

OpenAI API usage guide

🚀 MCP Weather Scraper

This project is an experimental implementation of the Model Context Protocol (MCP). It uses a lightweight LLM via OpenAI and FastAPI to retrieve and structure real - time weather information from public web sources. The goal is to explore how LLMs can interact with tools and act as intelligent agents to retrieve and reason about unstructured web data.

✨ Features

✅ MCP - compliant server for weather scraping via browser search
✅ Integration of OpenAI LLM (e.g., gpt - 3.5 - turbo)
✅ FastAPI server providing weather information as a callable MCP tool
✅ Efficient HTML parser using selectolax
✅ LLM handling unstructured web content extraction and conversion to a structured schema
✅ Streamlit application front - end for user interaction
✅ Response caching using functools.lru_cache

🧠 Prerequisite Concepts

Top 5 concepts to review for understanding this repository

🧩 Concept	🔍 What is it?	⚙️ Why is it important?
Model Context Protocol (MCP)	A new LLM tool - calling protocol	Enables structured AI agent communication
Uvicorn	A fast ASGI server for Python web applications	Hosts the FastAPI - based MCP server
Selectolax	A high - performance HTML parser	Efficiently scrapes and extracts weather data
`functools.lru_cache`	A built - in Python decorator for caching function calls	Improves performance by avoiding redundant fetches
Token Usage Metrics (OpenAI)	Token usage in LLM calls	Monitors and optimizes API costs

📦 File Structure

.
📁 mcp_weather_scraper/
│
├── assets/
│   └── streamlit_screenshot.png
├── server.py          # MCP - compatible tool server
├── client.py          # MCP client for interacting with the model
├── data_models.py     # Pydantic models for requests/responses
├── utils.py           # Utility functions for HTML cleaning, scraping, etc.
├── requirements.txt
└── .env

📄 License

This project is open - sourced under the MIT License.

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