This paper describes MorphoFiltered-Gemini, a multilingual system submitted to the PARSEME 2.0 shared task on multiword expression (MWE) identification. The system relies on Google Gemini 2.0 Flash-Lite to generate MWE predictions using zero-shot and selectively applied few-shot prompting, without fine-tuning or language-specific resources. To reduce the tendency of large language models to over-generate MWEs, we introduce a lightweight morphological post-filter that removes unlikely constructions while preserving high-precision patterns.Rather than optimizing peak performance for individual languages, our approach prioritizes precision and cross-lingual robustness. As a result, the system exhibits stable behavior across 17 typologically diverse languages and achieves the highest Shannon evenness score among all submitted systems. The experimental results highlight a clear trade-off between recall-oriented LLM prompting strategies and precision-oriented filtering, and show that simple linguistic constraints can effectively improve the stability of LLM-based multilingual MWE identification systems.