The 'ODD' (Overview, Design Concepts, Details) protocol was introduced in 2006 to standardize the documentation of individual- and agent-based models (ABMs). The primary goal of ODD is to enhance the clarity and completeness of model descriptions, making ABMs more reproducible and less subject to criticism. This review evaluates the use of ODD in over 50 publications, identifying areas for improvement and unanticipated benefits. The protocol has been widely adopted, with 75% of publications following it almost correctly or completely. However, several elements, such as "Input" and "Design Concepts," were often omitted or misinterpreted. The updated ODD protocol clarifies these issues and adds two new design concepts: "Basic Principles" and "Learning." The updated protocol aims to improve the rigor of model formulation, facilitate reviews and comparisons of ABMs, and promote more holistic approaches to modeling and theory. Despite its limitations, ODD shows promise as a standard for describing complex models, with increasing citations and use in the scientific community.The 'ODD' (Overview, Design Concepts, Details) protocol was introduced in 2006 to standardize the documentation of individual- and agent-based models (ABMs). The primary goal of ODD is to enhance the clarity and completeness of model descriptions, making ABMs more reproducible and less subject to criticism. This review evaluates the use of ODD in over 50 publications, identifying areas for improvement and unanticipated benefits. The protocol has been widely adopted, with 75% of publications following it almost correctly or completely. However, several elements, such as "Input" and "Design Concepts," were often omitted or misinterpreted. The updated ODD protocol clarifies these issues and adds two new design concepts: "Basic Principles" and "Learning." The updated protocol aims to improve the rigor of model formulation, facilitate reviews and comparisons of ABMs, and promote more holistic approaches to modeling and theory. Despite its limitations, ODD shows promise as a standard for describing complex models, with increasing citations and use in the scientific community.