Large language models are increasingly used in mental health applications, where responses must satisfy not only general safety requirements but also context-dependent ethical, professional, and jurisdictional constraints. We present a training-free inference framework for the MultiPsyche-2026 PsychEthicsEval shared task. For multiple-choice questions (Task I), the system uses ethics-aware structured prompting with independent option assessment and constrained machine-readable outputs. For open-ended questions (Task II), we introduce an identity-grounded and permission-bounded retrieval-augmented generation pipeline over curated Australian mental-health ethics resources. On the official hidden MCQ test set consisting of 282 samples, our system achieved a Partial Credit score of 0.768 (95% CI: 0.718–0.814) and an Exact Match score of 0.762. On 2,512 OEQ responses, the system obtained an ethical pass rate of 0.796 under the “rule pre-screening + classifier” evaluation and 0.621 under the independent LLM-as-a-Judge evaluation. The difference between the two OEQ evaluation scores indicates that the measurement results of open-ended ethical responses are influenced by how ethical violations are operationalized and evaluated.