Monte Carlo tree search (MCTS) is one of the most capa- ble online search algorithms for sequential planning tasks, with sig- nificant applications in areas such as resource allocation and transit planning. Despite its strong performance in real-world deployment, the inherent complexity of MCTS makes it challenging to understand for users without technical background. This paper considers the use of MCTS in transportation routing services, where the algorithm is integrated to develop optimized route plans. These plans are required to meet a range of constraints and requirements simultaneously, fur- ther complicating the task of explaining the algorithm’s operation in real-world contexts. To address this critical research gap, we intro- duce a novel computation tree logic-based explainer for MCTS. Our framework begins by taking user-defined requirements and translat- ing them into rigorous logic specifications through the use of lan- guage templates. Then, our explainer incorporates a logic verifica- tion and quantitative evaluation module that validates the states and actions traversed by the MCTS algorithm. The outcomes of this anal- ysis are then rendered into human-readable descriptive text using a second set of language templates. The user satisfaction of our ap- proach was assessed through a survey with 82 participants. The re- sults indicated that our explanatory approach significantly outper- forms other baselines in user preference.