description of continuous probability distribution The minimum description length of the realization of a random variable $x$ is given by the entropy of its probability mass function. It can be asymptotically communicated at this rate with an optimal coding. But that's for just one $x$ from the optimal coding table. How do we cover the whole support efficiently? (Of course, we'll have issues with quantization of real valued $x$-s in our code table, however optimized it's going to be.)

Viewing it differently, entropy of the representation of a generic probability distribution with CDF $F(x)$, where $F(x) \sim p(F(x))$ on some space of distributions $D$, seems to be bounded from above by entropy of uniform $p$ on space $D$: $-\int_D{p(F(x))\ln{p(F(x))}dF(x)}$. But this is clearly not a sharp maximum description length bound (probably need to introduce some reasonable restrictions).

Looks like I'm missing something obvious, any ideas would be appreciated...

Oh, and this has nothing to do with differential vs discrete entropy, the same question can be posed for the latter case.

[Updated after Peter Shor's comments; also cleaned up language and formulations a bit.]
You're confusing the Shannon entropy of a discrete probability distribution with the differential entropy of a continuous probability distribution. The minimum distribution length is only given by the Shannon entropy for discrete probability distributions.

What the fact that the differential entropy is 0 for this probability distribution means is that for large $k$, you can approximate this random variable with an expected error of $1/2^k$ using $k$ bits. If the differential entropy were $\ell$, you could approximate it with an error of $1/2^k$ using $k+\ell$ bits.