Context management remains a critical bottleneck in multi-agent Geospatial Artificial Intelligence systems, limiting task completion rates and causing context overflow in complex workflows. We propose ContextGeo, a systematic Context Engineering framework that integrates four Retrieval-Augmented Generation strategies through five specialized agents coordinated by a Spatial Context Manager. The framework introduces three novel mechanisms: Hierarchical Spatial Memory for structured multi-scale retrieval, geospatial-aware compression that preserves topological relationships, and Temporal Context Versioning for maintaining spatial-temporal consistency across workflow stages. Through comprehensive experiments on 150 real-world geospatial tasks spanning three complexity levels, ContextGeo demonstrates substantial performance improvements with 91.2% Task Completion Rate (+34.7% points over state-of-the-art), 87.6% Spatial Analysis Accuracy (+28.5% points), and 42.3% token reduction, all statistically significant at \(p < 0.001\) . These results provide empirical evidence that systematic Context Engineering can address fundamental limitations in multi-agent Geospatial Artificial Intelligence, with immediate implications for urban planning, environmental monitoring, and disaster response applications.