The Model Context Protocol (MCP) is a standardized interface designed to enable seamless interaction between AI models and external tools and resources, breaking down data silos and facilitating interoperability across diverse systems. This paper provides a comprehensive overview of MCP, focusing on its core components, workflow, and the lifecycle of MCP servers, which consists of three key phases: creation, operation, and update. We analyze the security and privacy risks associated with each phase and propose strategies to mitigate potential threats. The paper also examines the current MCP landscape, including its adoption by industry leaders and various use cases, as well as the tools and platforms supporting its integration. We explore future directions for MCP, highlighting the challenges and opportunities that will influence its adoption and evolution within the broader AI ecosystem. Finally, we offer recommendations for MCP stakeholders to ensure its secure and sustainable development as the AI landscape continues to evolve.

As a pivotal extension of the renowned ChatGPT, the GPT The development of Large Language Models (LLMs) has been Store serves as a dynamic marketplace for various Generative a transformative force in human life, reshaping interactions, Pre-trained Transformer (GPT) models, shaping the frontier enhancing communication, and influencing decision-making of conversational AI. This paper presents an in-depth measurement processes. A notable manifestation of this impact is ChatGPT, study of the GPT Store, with a focus on the categorization which, since its inception, has garnered widespread popularity, of GPTs by topic, factors influencing GPT popularity, evidenced by its millions of active users and its profound and the potential security risks. Our investigation starts with integration into various sectors such as education, business, assessing the categorization of GPTs in the GPT Store, analyzing and entertainment [17]. This surge in popularity not only how they are organized by topics, and evaluating the highlights the effectiveness of ChatGPT in understanding effectiveness of the classification system. We then examine and generating human-like text but also underscores the the factors that affect the popularity of specific GPTs, looking growing public interest in AI-driven solutions.