Developers often search for appropriate APIs to complete programming tasks during software development. Many API recommendation approaches and tools have been proposed to improve the efficiency of software development, but most of these approaches do not consider user interaction information. In this study, we propose an API recommendation tool based on client/server architecture and integrate it into the VS Code IDE in the form of a plug-in. In our tool, initial API recommendation lists are generated by existing tools. Learning-to-rank and active learning techniques are used, combined with user feedback information, to re-rank the API recommendation lists, achieving personalized recommendation. Extensive experiments demonstrate that the performance of this tool has been steadily improved with the increase in feedback data.