owl/community_usecase/Mcp_use_case/Readme.md

Content Curation with OWL & MCP

This project leverages OWL (Optimized Workforce Learning) and MCP (Multi-Agent Content Processing) to automate content curation. The system scrapes top tech news websites, extracts relevant information, and compiles a summary report.

Features

• Uses MCPToolkit for managing toolkits and performing web scraping.
• Implements OwlRolePlaying for enhanced multi-agent task execution.
• Scrapes TechCrunch, The Verge, and Wired.
• Extracts and summarizes headlines, article summaries, and publication dates.
• Generates a digest report (Latest_tech_digest.md) based on trends from these sources.

Installation

1. Clone this repository:

   git clone https://github.com/your-repo.git
   cd your-repo
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up environment variables:
   • Create a .env file and add your API keys/configuration as needed.

Usage

Run the script using:

python script.py "Your Custom Task Here"

Or use the default task defined in the script.

Configuration

• The script reads from mcp_servers_config.json to configure MCP.
• Modify the default_task section to adjust scraping and summarization behavior.

Error Handling

• The script ensures graceful cleanup in case of failures.
• Implements try-except blocks to handle tool execution errors.

Cleanup & Shutdown

• The script automatically disconnects MCP after execution.
• Cancels running async tasks to prevent memory leaks.
• Handles KeyboardInterrupt for a graceful shutdown.

Future Improvements

• Add support for more tech news sources.
• Implement NLP-based sentiment analysis on extracted news.
• Enable storing summaries in structured formats like JSON/CSV.