  We present a generator of random networks where both the degree-dependent
clustering coefficient and the degree distribution are tunable. Following the
same philosophy as in the configuration model, the degree distribution and the
clustering coefficient for each class of nodes of degree $k$ are fixed ad hoc
and a priori. The algorithm generates corresponding topologies by applying
first a closure of triangles and secondly the classical closure of remaining
free stubs. The procedure unveils an universal relation among clustering and
degree-degree correlations for all networks, where the level of assortativity
establishes an upper limit to the level of clustering. Maximum assortativity
ensures no restriction on the decay of the clustering coefficient whereas
disassortativity sets a stronger constraint on its behavior. Correlation
measures in real networks are seen to observe this structural bound.
