This paper evaluates the impact of expanding the UD_Nheengatu-CompLin treebank on parsing performance for Nheengatu, a Brazilian endangered Indigenous language. We hypothesized that the inclusion of annotated data would result in a 10% improvement in the Labeled Attachment Score (LAS). To test this hypothesis, we conducted a 10-fold cross-validation experiment using UDPipe 1.4 under two conditions: parsing with gold tokenization and gold tags, and automatic parsing from raw text. Statistical significance was determined using the Mann-Whitney U test. Although the expected gain was not achieved, the results show improvements in parsing accuracy and reduced variance across folds. The findings highlight the importance of corpus expansion and standardized annotation workflows for improving parsing performance in low-resource language scenarios and for supporting reproducible evaluation methods in the computational modeling of minority languages.