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MCP Weather Scraper
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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 dataDeveloper tools#Weather scraping#MCP protocol#LLM agent#Data parsing.Python
2 points
9.0K
Open Site

Overview

Content Details

Alternatives

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 search
Use AI technology to automatically search and extract weather information from the web without the need to know specific weather websites in advance.
Structured data output
Convert unstructured web weather information into standardized JSON format data.
Performance optimization
Use a high-speed HTML parser and cache mechanism to ensure fast response.
Usage statistics
Provide detailed token usage statistics to help understand AI processing costs.
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 planning
When developing a travel planning application, integrate the weather query function to help users understand the weather conditions at the destination.
Smart home
The 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

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

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