Decision Trees Should We Discard Low Importance Features?
As for many questions, the answer is "it depends":


features which have a low individual importance may still add predictive power to your model, because the model benefits from combining their information together with information of other features.
However they may introduce noise in the model and cause overfitting, thus decreasing the performance of the model.


The best way to answer your question is to experiment: 


order the features by decreasing importance
loop from say 5 features to 30, each time selecting the top N features by importance, and training/testing a model based on this subset of features.
plot the performance


You're likely to observe that the performance increases quite a lot at the start for each "important" feature added, then slows down as feature importance decreases and probably doesn't increase at all at some point, possibly even decreasing a bit.