What the clamping wants to achieve is to bound the Lipschitz norm L of the mapping provided by the discriminator, i.e. you want |D(x_fake)-D(x_real)|<=L |x_fake-x_real| for some reasonable L, for example L = 1.
You could output the norms above to check and if your architecture is so that the clamp to [-0.01,0.01] is not appropriate, do increase the bound.
While the Lipschitz constant enforced by the clamping interval is not terribly important for theoretical purposes, there is a subtle interaction between the magnitude of the discriminator output, the clamping and the size of the gradients / optimization steps. The WGAN-GP 14 article actually lists as one of their achievements that by controlling L directly, they reduce the necessity of tuning hyperparamters such as the clamping (even if they seem to need a different penalty one in the toy example, which I think stems from the non-convexity in the penalty 4).
Best regards
Thomas