Artificial Intelligence (AI) Planning has emerged as a crucial subfield of AI, providing effective methods to achieve specific goals through the selection and execution of actions. Despite the significant advancements, a systematic analysis of AI Planning tools, particularly regarding their functionalities, usability, and industrial readiness remains insufficient. This thesis bridges this gap by conducting a systematic multivocal literature review of both peer-reviewed and grey literature to evaluate the development and effectiveness of AI Planning tools. In this study, 63 AI Planning tools published between 2002 and 2024 were identified and analyzed based on key objectives, including their technical attributes, application domains, and challenges. This research provides a detailed classification framework for AI Planning tools and highlights trends, challenges, and gaps in the field. The results indicate that competitions—particularly the International Planning Competitions (IPCs) play a vital role in showcasing and advancing AI Planning tools. These events significantly contribute to innovation, with many tools building on older systems and frequently reusing algorithms and heuristics from earlier research. The findings offer actionable recommendations for researchers, practitioners, and educators to foster innovation, adoption, and real-world applicability of AI Planning tools.