How much does an LLM know about my programming language?
Large Language Models (LLMs) are increasingly used for code generation, yet their support for programming languages is uneven, particularly for low-resource languages and infrequently used language constructs. Existing evaluation methodologies primarily assess functional correctness through test execution, which fails to reveal blind spots in a model's knowledge of a target language. In this paper, we introduce a systematic framework to identify and quantify the syntactic knowledge exhibited by an LLM with respect to a given programming language. We define syntactic coverage as a set of measurable indicators of how comprehensively language constructs are represented in datasets and generated code. Our approach analyzes code snippets against grammatical rules and structural features to compute interpretable coverage metrics, detect underrepresented constructs, and expose gaps in language support. The proposed framework supports comparative analysis across datasets, languages, and models through an automated pipeline that produces actionable diagnostic reports. Our experimental results demonstrate that syntactic coverage analysis reveals substantial differences in language support that are not captured by functional correctness alone, enabling more informed model selection, benchmark design, and dataset improvement strategies.