In object-oriented programming, a method is pure if calling the method does not change object states that exist in the pre-states of the method call. Pure methods are widely-used in automatic techniques, including test generation, complier optimization, and program repair. Due to the source code dependency, it is infeasible to completely and accurately identify all pure methods. Instead, existing techniques such as ReImInfer are designed to identify a subset of accurate results of pure method and mark the other methods as unknown ones. In this paper, we designed and implemented MetPurity , a learning-based tool of pure method identification. Given all methods in a project, MetPurity labels a training set via automatic program analysis and builds a binary classifier (implemented with the random forest classifier) based on the training set. This classifier is used to predict the purity for all the other methods (i.e., unknown ones) in the same project. Preliminary evaluation on four open-source Java projects shows that MetPurity can provide a list of identified pure methods with a low error rate. Applying MetPurity to EvoSuite can increase the number of killed mutants in the test generation of EvoSuite. A demo video of this tool can be found at https://youtu.be/ Ac3cmjn4CCs/; the prototype and evaluation data are available at http://cstar.whu.edu.cn/p/metpurity/.