With the proliferation of online geospatial services, Geospatial Application Programming Interfaces (GeoAPIs) have become the backbone of modern spatial data interoperability. However, the high technical barriers of GeoAPIs, characterized by complex RESTful syntax and deterministic parameter requirements, create a significant "digital divide" for non-expert users. To bridge the gap between intuitive human spatial intent and technical service execution, this study proposes Text2GeoAPI, a novel conceptual framework for the automatic invocation and composition of geospatial services via natural language. We introduce the Intent-Entity- Operation (IEO) model to formalize spatial tasks, decoupling high-level semantic goals from atomic technical operations. We developed a modular prototype leveraging Large Language Models (LLMs) as cognitive engines to perform structured intent parsing, dynamic workflow planning, and multi-source result synthesis. Experimental evaluations using 100 diverse spatial queries demonstrate an overall task success rate of 86%, with the system effectively orchestrating multi-hop service chains (e.g., Geocoding → Isochrone Analysis → POI Search). The results confirm that Text2GeoAPI significantly lowers the threshold for accessing professional geospatial analysis, shifting the GIS paradigm from "tool-centric" to "intent-centric" intelligence.