How to interpret the Mean squared error value in a regression model?
Whether you model is performing well or not depends on your business case, you might hive tiny RMSE or great looking score on whatever metric you are using, but it just not enough to solve the business problem, in that case the model is not performing well.

MSE is just that Mean Squared Error

Both MSE and RMSE measure by how much the predicted result deviates from actual, because of the squared term more weight is given to larger errors, and because of square root in RMSE, it is in the same units as dependent variable. MAE, Mean Absolute Error is another useful metric to look at when you are evaluating a regression model; it is also easier to interpret.

Given your data, R-squared seems fine to me.