Optimization problems modeled in the AMPL modeling language [Fourer, Gay and Kernighan (2002)] may be examined by a set of tools found in the AMPL Library [Gay (2007)]. DrAmpl is a meta solver which, by use of the AMPL Library, dissects such optimization problems, obtains statistics on their data, is able to symbolically prove or numerically disprove convexity of the functions involved and provides aid in the decision for an appropriate solver. A problem is associated with a number of relevant solvers available on the NEOS Server for Optimization [Czyzyk, Mesnier and Moré (1998)] by means of relational database. We describe the need for such a tool, the design of DrAmpl and some of its consequences.