GIS MCP Server

The GIS MCP Server bridges the gap between conversational AI and geospatial analysis by exposing a rich set of GIS functions through the Model Context Protocol. Instead of relying on external APIs or manual data handling, developers can now ask an AI assistant to perform complex spatial operations—such as computing intersections, reprojecting coordinate systems, or generating NDVI indices—directly within the chat. This eliminates the need for separate GIS workflows and allows users to harness spatial intelligence in a natural, conversational manner.

At its core, the server wraps popular Python GIS libraries—Shapely, PyProj, GeoPandas, Rasterio, and PySAL—into MCP‑compatible tools. Each library’s core capabilities are mapped to callable functions that accept JSON‑structured arguments and return results in a format the AI can interpret. This design gives developers the flexibility to leverage mature, battle‑tested geospatial algorithms without exposing the underlying complexity to end users. The server also provides specialized utilities for visualizing data, generating static or interactive maps, and accessing administrative boundaries, climate datasets, ecological layers, movement tracks, land cover maps, and satellite imagery.

Key features include:

• Geometry operations such as union, intersection, buffer, and difference that enable spatial reasoning about points, lines, and polygons.
• Coordinate transformation tools for reprojecting geometries between any CRS supported by PyProj, ensuring analyses are performed in the correct spatial reference.
• Raster processing capabilities like clipping, resampling, and index calculation (e.g., NDVI), allowing AI assistants to manipulate satellite imagery on the fly.
• Spatial statistics through PySAL, providing autocorrelation metrics, clustering, and neighborhood analysis for advanced spatial modeling.
• Visualization helpers that generate static maps or interactive web maps, enabling instant visual feedback within the chat.

Real‑world scenarios that benefit from this server include environmental monitoring (e.g., assessing deforestation hotspots), urban planning (e.g., evaluating service area overlaps), disaster response (e.g., mapping flood extents), and location intelligence for marketing or logistics. By integrating seamlessly with MCP‑compatible clients such as Claude Desktop or Cursor IDE, developers can embed these spatial tools into existing AI workflows with minimal friction. The server’s extensible architecture also allows custom tool additions, making it a versatile foundation for any project that requires spatial context in conversational AI.