Traditional spatial databases and map services handle deterministic queries well, but struggle to handle intent-rich and complex spatial queries. Even a simple request such as “In Melbourne, find a convenient petrol stop along the Tullamarine Freeway to the airport, avoiding tolls and minimizing detour” often breaks current systems, revealing a gap between natural-language intent and executable spatial operations. To fill this gap, we introduce an LLM-driven framework that parses user intent, decomposes complex spatial requests into executable sub-queries, schedules the necessary Google Maps API calls, and synthesizes the final answer. To enable systematic study, we curate a dataset of complex spatial queries with selected answers and provide a concrete implementation that compiles intents into routes and places calls while coordinating routing and POI services. We evaluate on the generated dataset with Google Maps and find that our system substantially outperforms a zero-shot LLM: it retrieves more feasible, on-route candidates and produces schema-valid, verifiable answers, while trading modest extra latency.