Abstract: In today’s AI-driven world, unlocking AI potential and enabling AI models to communicate with external data sources is vital for enhancing the efficiency and security of AI-driven applications. The Model Context Protocol (MCP) serves as a standard for maximizing AI potential. This study leverages a machine learning approach to predict the effectiveness of the MCP Authorization Model for an LLM-powered agent. It utilizes logs from Azure services such as Azure Monitor, Azure Sentinel, and Azure Active Directory, which are used to monitor MCP server activity, to create a sample dataset. This dataset includes features such as source_ip, destination_ip, event_type, alert_severity, and target_variable. These features are used to train the ML model to assess the effectiveness of the MCP Authorization model for LLM-powered agents, enabling organizations to better understand the importance of a secure connection between AI models. This approach contributes to unlocking AI’s full potential while improving application security and operational efficiency.

Upakar Bhatta. “ML to Predict Effectiveness of the MCP Authorization Model for LLM-Powered Agent”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161201

@article{Bhatta2025,
title = {ML to Predict Effectiveness of the MCP Authorization Model for LLM-Powered Agent},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161201},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161201},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Upakar Bhatta}
}