Accurate determination of the pathogenicity of missense genetic variants of uncertain significance is a huge challenge for implementing genetic data in clinical practice. In silico predictive tools are used to score variants' pathogenicity. However, their value in clinical settings is often unclear since they have usually not been validated against robust functional assays. We compare nine widely used in silico predictive tools including more recently developed tools (EVE and REVEL) with detailed cell-based electrophysiology for 126 CLCN1 variants discovered in patients with the skeletal muscle channelopathy myotonia congenita. We found poor accuracy for most tools. The highest accuracy was with Mutation Taster (84.58%) and REVEL (82.54%). However, both scores have poor specificity. EVE has better specificity. Combined methods based on concordance, improved performance overall but still lacked specificity. Our calculated statistics for the predictive tools are different to reported values for other genes in the literature suggesting that utility of the tools varies between genes. Overall, current predictive tools for this chloride channel are not reliable for clinical use and tools with better specificity are urgently required. Improving the accuracy of predictive tools is a wider issue and a huge challenge for effective clinical implementation of genetic data.