Decision makers must have sufficient confidence in models if they are to influence their decisions. We propose three screening questions to critically evaluate models with respect to their purpose, organization, and evidence. They enable a more transparent, robust, and secure use of model outputs. Models are increasingly used to support decisions across environmental, economic, social, and public health issues. They deliver insights and possible solutions to real-world problems and allow decision makers to evaluate the consequences of their decisions before implementation. Examples include simulations of financial markets, fisheries, climate, and the spread of infectious diseases. On the one hand, models have helped make effective decisions, for example, in the eradication of rabies 1,2. On the other hand, models have sometimes been trusted with disastrous consequences 3 such as the collapse of the Atlantic cod fishery and the 2008 financial crisis. As the world faces the uncertainty of the Covid-19 pandemic, models represent the most effective tool to identify interventions that can balance the risks of widespread infection and social disruption until an effective treatment is established 4. Contradictory outputs from numerous models, however, reflect widely differing assumptions and purposes 5. Divergent model outputs are not only confusing but underscore the need for clear communication of models and their context, so that decision makers can select the most appropriate models for the problem at hand. Model communication has been previously addressed within the context of good modeling practice (GMP 6-8). GMP, however, does not necessarily reflect the perspective of decision makers who at some point need to trust models and their output if they are to influence their decisions 9. Decision makers want evidence that a model works, which relies on demonstrating the model's realism and the reliability of data inputs and key assumptions. Here, we integrate central principles from GMP with the decision makers' perspective to propose three screening questions (Fig. 1). The questions provide an overview of a model's purpose, the assumptions underlying its organization, and the evidence that it is realistic enough for its purpose. Based on the ODD protocol for communicating models 10 , these questions should be addressed by modelers during model development, and by decision makers before a model's outputs are used for decision support.