Serverless computing has gained widespread adoption due to its scalability, cost-efficiency, and abstraction of infrastructure management. However, the shift toward event-driven, function-based architectures introduces new code quality challenges and development practices that differ from traditional paradigms. While recent research has identified serverless-specific bad practices commonly referred to as “smells,” there remains a lack of automated tools to support their detection and remediation. This paper presents ServLessSense, a tool designed to detect code smells automatically in serverless applications written in JavaScript and TypeScript. Built using a custom ESLint plugin, ServLessSense identifies five serverless-specific smells, provides visualizations through an interactive dashboard, and integrates Large Language Models to offer automated refactoring suggestions. We evaluated the precision of the tool using five open-source serverless applications and conducted a pilot survey study to assess its potential usefulness from the practitioners’ perspective. The results indicate that ServLessSense is helpful in detecting serverless-specific smells and generating refactoring suggestions. The survey participants showed an overall favorable perspective towards ServLessSense.