Interlinear glossing is essential for the study and revitalization of endangered languages. However, it remains a time-consuming process that requires extensive linguistic expertise. Recent advances in Large Language Models (LLMs) offer a potential solution. In this research, we study the case of Mapudungun, an endangered language spoken in Chile and Argentina, to generate automatic interlinear glosses using the Gemini 2.5 Pro model. Our study investigates which information configuration through Retrieval-Augmented Generation (RAG) yields the best results. We compare the integration of a formal grammar, a dictionary, a small annotated corpus, and a combination of all these resources. Our evaluation shows that while dictionary integration causes a significant degradation in performance, grounding the model with a structured corpus maximizes accuracy relative to the resources employed. Notably, we find that a remarkably small dataset of 589 meaning units provides enough normative guidance to significantly improve the morphological tagging task. This work highlights the viability of utilizing minimally annotated corpora to assist in the documentation of morphologically complex languages.