Microbial communities play a pivotal role in a wide range of ecological processes and engineered systems. The level of complexity in analyzing microbial communities necessitates the use of predictive mathematical models such as genome-scale metabolic networks. To identify metabolic interdependence among member species within microbial communities, we present a recently developed computational tool combining two existing approaches: SteadyCom and Flux Coupling Analysis (FCA). Both approaches leverage genome-scale metabolic networks as their primary inputs, albeit for distinct objectives. SteadyCom is a community modeling tool used to estimate flux distributions and metabolite exchanges within and across species, respectively, by constraining individual specific growth rates to be equal. In contrast, FCA has been used to identify the causal relationships among reactions with a primary focus on the analysis of individual metabolic networks, i.e., what reactions must be active for a target reaction to be active. The combination of these two approaches allows us to find how metabolic reactions in individual species are coordinated as an interacting community. Without any additional computations, the implementation of this algorithm also provides information on what reactions are blocked in the network. We provide all the information needed to implement these coupled computational tools in a Python Jupyter notebook.