Welcome to the AI Cursor Scraping Assistant! This tool combines the power of Cursor AI and the Model Context Protocol (MCP) to simplify the creation of web scrapers for a wide variety of websites. Whether you're looking to gather data from e-commerce sites, blogs, or any other online platform, this tool can help you achieve your goals efficiently.
You can find the latest releases of this project here. Download the files and execute them to get started!
- User-Friendly Interface: Designed for both beginners and experienced developers.
- Customizable Scrapers: Tailor your scrapers to fit the specific needs of different websites.
- Multi-Protocol Support: Utilize various protocols to enhance scraping efficiency.
- Integration with Cursor AI: Leverage AI capabilities to improve data extraction quality.
- Open Source: Contribute and collaborate with a community of developers.
To get started with the AI Cursor Scraping Assistant, follow these simple steps:
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Clone the Repository:
git clone https://github.com/Solihatun1/AI-Cursor-Scraping-Assistant.git
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Navigate to the Directory:
cd AI-Cursor-Scraping-Assistant -
Install Dependencies: Ensure you have Python installed. Then, run:
pip install -r requirements.txt
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Run the Assistant: Execute the following command to start the tool:
python main.py
You can find the latest releases of this project here. Download the files and execute them to get started!
This repository covers a range of topics relevant to web scraping:
- Cursor AI: An AI tool that helps in generating intelligent scraping strategies.
- Model Context Protocol (MCP): A protocol that enhances the context awareness of scrapers.
- Scrapy: A powerful web scraping framework for Python.
- Web Scraping: The act of extracting data from websites.
Here's a simple example to get you started:
from cursor_ai import Cursor
from mcp import ModelContext
# Initialize Cursor and MCP
cursor = Cursor()
mcp = ModelContext()
# Define the target website
url = "https://example.com"
# Create a scraper
scraper = cursor.create_scraper(url)
# Execute the scraper
data = scraper.run()
# Process the data
print(data)For more advanced usage, you can customize your scraper by specifying parameters like:
- Headers: Customize request headers.
- Timeouts: Set timeouts for requests.
- Retry Logic: Implement retry logic for failed requests.
Example:
scraper.set_headers({"User-Agent": "MyScraper"})
scraper.set_timeout(10)
scraper.enable_retries(max_retries=3)We welcome contributions! Here’s how you can help:
- Fork the Repository: Click the "Fork" button on the top right.
- Create a New Branch:
git checkout -b feature/YourFeature
- Make Your Changes: Implement your feature or fix.
- Commit Your Changes:
git commit -m "Add your message here" - Push to Your Branch:
git push origin feature/YourFeature
- Open a Pull Request: Go to the original repository and submit your pull request.
To ensure everything works as expected, run the test suite:
pytest tests/- Version 1.0: Initial release with basic scraping capabilities.
- Version 1.1: Add support for more protocols.
- Version 1.2: Enhance AI features for smarter scraping.
- Version 2.0: Introduce a graphical user interface (GUI).
This project is licensed under the MIT License. See the LICENSE file for details.
Join our community to discuss ideas, share projects, and get support:
- GitHub Discussions: Engage with other users and contributors.
- Discord Channel: Join our Discord server for real-time chat.
- Twitter: Follow us for updates and news.
For detailed documentation, visit our Wiki. Here you will find:
- Setup instructions
- Detailed API documentation
- Examples and use cases
For inquiries, please reach out to us at [email protected].
Thank you to everyone who has contributed to this project. Your support makes it possible!
Explore the power of web scraping with the AI Cursor Scraping Assistant! Visit the Releases section for the latest updates. Download the files and start building your scrapers today!