This paper proposes a method to improve spatial understanding in LLM-driven agents by combining geometrical tools with LLMs. The approach involves vector-quantizing geodesic trajectories and associating natural language descriptions with them. This allows the LLM to choose the most appropriate tool for a given state and goal, effectively separating learning into two levels: tool discovery and reasoning.