The evolution of software automation frameworks in the era of cloud computing and continuous delivery has redefined how quality assurance systems are designed and executed. Traditional test automation tools, while effective for isolated scenarios, lack the contextual adaptability required for dynamic, large-scale cloud ecosystems. Playwright, an advanced open-source testing framework, provides robust cross-browser and multi-platform automation, yet its native capabilities do not fully exploit the elastic nature of cloud infrastructure or the intelligent orchestration potential of distributed environments. This paper presents a novel approach that integrates Playwright with Model Control Protocol (MCP) servers to establish a context-aware automation framework capable of dynamically adapting to environmental variables, system load, and real-time telemetry. The proposed MCP-driven architecture introduces predictive orchestration and context learning mechanisms that intelligently allocate and optimize test workloads across heterogeneous cloud nodes. By leveraging cloud-native functionalities—including auto-scaling, resource pooling, and parallel execution pipelines—the framework achieves optimal utilization of compute resources while maintaining high reliability, fault tolerance, and adaptive recovery. Experimental validation conducted on Google Cloud Platform (GCP) demonstrates measurable improvements: a 37% increase in execution speed, a 22% reduction in resource consumption, and a consistent 18% improvement in failure recovery rates compared to baseline Playwright deployments. Moreover, the system’s telemetry-driven scheduling enables predictive reruns and reduces redundant test executions under fluctuating network conditions. While the results highlight significant efficiency and adaptability gains, limitations remain in Large Language Model (LLM) -driven orchestration interpretability, security of multi-agent coordination, and computational overhead in large-scale deployments. Future work will focus on developing a quantitative MCP–LLM-Agent prototype, conducting cross-cloud benchmarking, and enhancing explainability in AI-driven orchestration models. This research positions MCP-assisted Playwright automation as a pivotal step toward self-optimizing, autonomous quality engineering ecosystems, paving the way for next-generation AI-augmented DevSecOps workflows.

Playwright, MCP servers, Cloud automation, Context-aware testing, DevOps, Intelligent orchestration