Applying unconstrained generative protein models to fixed structural scaffolds can produce systematic design artifacts, including glycine enrichment, grammar violations, and discordant complex predictions. We developed DARPinMPNN, a scaffold-constrained computational framework that restricts production sequence generation to permitted DARPin Greek Grammar residue pools. A state-aware chimeric multiple sequence alignment strategy supported AlphaFold2-Multimer (AF2) screening, followed by complementary AlphaFold 3 (AF3) evaluation. Of 15,000 generated variants targeting mesothelin, VEGF-A, and HER2, 149 library designs and five separately analyzed controls had deposited 25-model AF3 results. Using the unrounded median of 25 corrected target-DARPin Ranking Scores, eight library designs passed the threshold of 0.70, all targeting mesothelin, corresponding to 0.053% of the generated library. The highest-ranked design had median corrected RS 0.794; the G3 positive control passed separately with median corrected RS 0.866. AF3 median chain-pair ipTM was lower than AF2 ipTM for 148 of 149 library designs. These results demonstrate target-dependent computational prioritization and cross-predictor discordance, not experimental binding or false-positive rates. The candidates require biophysical and cellular validation.