Surgical tool classification and instance segmentation are crucial for minimally invasive surgeries and related applications. Though most of the state-of-the-art for instance segmentation in natural images use transformer-based architectures, they have not been successful for medical instruments. In this paper, we investigate the reasons for the failure. Our analysis reveals that this is due to incorrect query initialization, which is unsuitable for fine-grained classification of highly occlude objects in a low data setting, typical for medical instruments. We propose a class-agnostic Query Proposal Network (QPN) to improve query initialization inputted to the decoder layers. Towards this, we propose a deformable-cross-attention-based learnable Query Proposal Decoder (QPD). The proposed QPN improves the recall rate of the query initialization by 44.89% at 0.9 IOU. This leads to an improvement in segmentation performance by 1.84% on Endovis17 and 2.09% on Endovis18 datasets, as measured by ISI-IOU.