Application tool recommendations are described. Initially, application usage data is captured indicating tools used and actions performed by existing users of an application. This application usage data is converted into human-readable words describing the tools used and actions performed. This allows natural language processing techniques to be applied to the converted data. Through natural language processing, importance scores for the tools and actions can be computed and tasks performed with the application determined. The natural language processing techniques are also used to build task prediction models based on the importance scores and determined tasks. These task prediction models indicate probabilities of the determined tasks to be next performed by a current application user. A task having the highest probability of being next performed is predicted as the next task. Tool recommendations associated with the predicted next task are then presented to aid the user with the predicted next task.