How we built a GIS agent on Databricks with Felt's MCP server

Felt and Databricks describe building a GIS-focused AI agent intended to let users ask spatial questions in natural language and receive live, interactive maps rather than static exports, code snippets, or unverified answers. In a demonstration using Databricks Agent Bricks and Felt’s MCP server, subagents identified relevant Arizona energy datasets, used spatial SQL through Lakebase’s PostGIS capabilities to find transmission infrastructure near active wildfires, and created a layered map of fire perimeters, lines, substations, burn probability, and solar assets in Slack. The architecture combines the Lakehouse for large-scale analytics with Lakebase for fast conversational geospatial queries, while Unity Catalog provides shared governance, permissions, lineage, and query traces. The approach is aimed at engineers, analysts, and platform teams that need governed mapping without building separate GIS workflows, although the authors emphasize that human expertise remains necessary to choose appropriate datasets, interpret results, and make decisions.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.