Computing the condition number of a matrix is an expensive operation (for example, involving computing the largest and smallest magnitude eigenvalues, or solving several(!) linear systems), so doing this by default in a call to a linear solver would be very wasteful. It makes much more sense to just try to solve the system, and in case the solver breaks down due to ill-conditioning, catch that error and terminate the routine gracefully (with a helpful error message warning of ill-conditioning). In addition, some iterative solvers (most notably GMRES) produce an estimate of the condition number as a by-product. Since PARDISO is a parallel direct solver based on LU or Cholesky decomposition, this is not available.
For the rare cases when one wants to know the condition number without solving a linear system, dedicated routines exist. For example, LAPACK (which is bundled in Intel's MKL) provides the
???con routines (see https://software.intel.com/en-us/node/520910) for dense matrices. Routines for estimating (computing would be infeasible) the condition number of large sparse matrices (based on Krylov methods for computing approximate eigenvalues) also exist, but are not bundled in the MKL. If this is what you need to do, you have to look at one of the dedicated packages for sparse linear algebra such as PETSc (see How can I estimate the condition number of a large sparse matrix using PETSc?) or Trilinos (look for IFPACK in the documentation).