Mobility-as-a-Service (MaaS) represents a different view on how individuals approach transportation, allowing them to integrate various travel modes into a unique subscription, often referred to as a MaaS package/ bundle. This study aims to present an innovative tool designed to collect dynamic stated preferences (SP) data in the context of MaaS. The tool has been designed to collect individual data of respondents, allowing them to customize their MaaS mobility bundles and dynamically reorganize their trip chains based on the selected transport modes. The tool will serve as a basis to collect data with the purpose of training a hybrid Recommendation System architecture, with a double purpose: a) suggesting personalized MaaS packages and b) optimizing real-time single trip choices based on subscribed mobility packages. A case study based on the urban context of Naples (Italy) is presented, highlighting the app's unique components, mainly linked to the transport supply characteristics of the context. The successive analysis of the data will provide valuable insights on user behavior preferences, willingness to adopt and pay for MaaS services, as well as the possibility to perform system-level evaluation based on the collected micro-data.