If you truncate your priors you’ll also want to constrain your variables (to prevent the sampler from going outside of the support of the truncated distribution – this’ll give HMC trouble), so that doesn’t get around the problem.
Vectorized constraints aren’t here yet, but for what you’re describing you might be better off just rolling your own: How to specify variable parameter constraints based on other model parameters . Check out the constraints part of the manual: https://mc-stan.org/docs/2_18/reference-manual/variable-transforms-chapter.html for how this is done.
Also, since your priors aren’t a function of other parameters, you really don’t need that truncation either. The normalization for the truncation is constant so you can leave it out.
Hope that helps!