Multi-spectral algorithms are being used at NASA Langley to retrieve microphysical cloud properties from satellite imagery in near-real time over a variety of domains. Currently, the Solar infrared-Infrared-Split window Technique (SIST) is applied to night time imagery from the Geostationary Operational Environmental Satellite (GOES) over the Continental US, the Spinning Enhanced Visible InfraRed Imager (SEVIRI) over Europe, and the Multi-functional Transport Satellite (MTSAT) over the tropical western Pacific and to Moderate Resolution Imaging Spectroradiometer (MODIS) imagery over the globe. The SIST is being modified for integration into the GOES-R cloud application framework via the Geostationary Cloud Algorithm Testbed (GEOCAT), which will allow input and feedback opportunities from other cloud application team baseline algorithms. This paper will present results from new SIST retrievals conducted on 3.9, 10.8 and 12-µm SEVIRI imagery that is being used as a proxy for Advanced Baseline Imager (ABI) data. SIST-derived cloud optical depth, effective particle size and liquid/ice water path derived within the GOES-R cloud application team's developmental framework will be presented. Included will be an assessment of the impact of allowing cloud parameters, such as cloud temperature and phase that have previously been derived within SIST itself, to be determined by other cloud application team algorithms prior to the invocation of SIST. These assessments will be conducted for both case studies and larger datasets. Potential enhancements to the SIST, including the use of 8.7 and 13.3-µm data will also be examined. GEOCAT will be used to facilitate the evaluation of the strengths of both the stand-alone and modified versions of SIST, thus allowing inter-algorithm comparisons that will be used to guide the application team as they optimize the GOES-R algorithms.