FAIR principles play a crucial role in advancing the reproducibility of metabolomics workflows by promoting interoperability, sharing, and reuse. The complex nature of metabolomics workflows integrating various components necessitates working towards better implementations of the FAIR principles to improve the reliability and consistency of results. This tutorial demonstrated an application of the FAIR principles using the Metabolome Annotation Workflow (MAW) as an example, showcasing the role of WorkflowHub as a FAIR workflow registry and the utilisation of the Common Workflow Language (CWL) to enable standardised practices and interoperability across different platforms. By embracing FAIR principles and establishing minimum standards and ontologies for metabolomics workflows and the associated provenance, we can increase reproducibility, facilitate effective data sharing, improve interoperability by reducing the dependency on specific workflow engines or languages, and foster further advancements in computational metabolomics.