SibFly API lets AI agents query real satellite ground-subsidence data by address

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A tool called SibFly now allows AI agents built with LangChain to retrieve measured ground-motion data for any US address, returning vertical displacement rates in millimeters per year. The data is sourced from NASA's OPERA Sentinel-1 InSAR satellite dataset, which tracks sub-centimeter ground movement across North America. Unlike flood-zone or soil models, this signal reflects actual physical measurements, making it useful for screening properties at risk of foundation damage from ground subsidence. Developers can integrate the tool in roughly 30 lines of code, with queries priced at $0.40 per covered result and no charge for out-of-coverage or low-confidence responses. SibFly also offers a hosted MCP server, enabling any compatible AI client to access the ground-motion data without requiring a dedicated SDK.

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