A tag-set for tagging Luganda words has been in absence for quite a long time leading to absence of Luganda Language resources used in Computational Linguistic (CL) and Natural Language Processing (NLP). As a result, this research paper proposes and presents a Structured Compact Tag-set for Luganda (SCTL) in a bid to address this gap, and emphasis has been directed towards presenting the structures. SCTL incorporates a number of new concepts aimed at reducing redundancy in an annotated corpus. In line with this, Tag Length Minimization Strategies (TLMS) have been proposed and implemented in SCTL. The morpho-syntactic properties captured in SCTL were identified through conducting a morphological analysis and word categorizaPhonology, Morphologytion along the various parts of speeches (POS) of Luganda. To demonstrate the suitability of SCTL to tag Luganda text, a sample text extracted from Bukedde, an online Luganda news paper, has been tagged and presented; however, identification and validation of the various tags of SCTL is proposed as a component of continuity of this research work. This paper demonstrates how Concord Number (CN) captured in SCTL can be used to check conventional agreement between words. Storage Efficiencies, namely, ηt (individual) and ηat (batch) are novel metrics proposed in this research work, which can be used in evaluating how a particular tag-set is performing in terms of efficient storage usage at tag level and corpus (or batch) level respectively. Finding on the comparison of Storage Efficiencies (ηt and ηat) of tags from the four tag-sets of Luganda, Swahili, Russian and Northern Sotho, show that SCTL tags had the highest ηt therefore the highest ηat among the tags considered, due to the application of TLMS which maximizes ηt . Finding on the impact of TLMS on these tag-sets using Storage Efficiencies (ηt and ηat) as evaluation metrics show that there was a three-folds improvement to Swahili tags, a two-fold to Russian tags, and a 60\% to Northern Sotho tags. SCTL is associated with a number of advantages and have been presented herein. Conclusively, the advent of SCTL has opened the avenue of developing other NLP resources, especially, an annotated Luganda corpus. TLMS is very crucial in highly inflectional languages which have a lot of inherent morpho-syntactic information to capture, in bid to boost their tag storage efficiencies.