Automatic error type classification is an important process in both learner corpora creation and evaluation of large-scale grammatical error correction systems. Rule-based classifier approaches such as ERRANT have been widely used to classify edits between correct-erroneous sentence pairs into predefined error categories. However, the used error categories are far from being universal yielding many language specific variants of ERRANT.In this paper, we discuss the applicability of the previously introduced grammatical error types to an agglutinative language, Turkish. We suggest changes on current error categories and discuss a hierarchical structure to better suit the inflectional and derivational properties of this morphologically highly rich language. We also introduce ERRANT-TR, the first automatic error type classification toolkit for Turkish. ERRANT-TR currently uses a rule-based error type classification pipeline which relies on word level morphological information. Due to unavailability of learner corpora in Turkish, the proposed system is evaluated on a small set of 106 annotated sentences and its performance is measured as 77.04% F0.5 score. The next step is to use ERRANT-TR for the development of a Turkish learner corpus.