$ loading_
Enable AI agents to safely run GDAL and Rasterio geospatial raster operations.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "gdal-mcp" yet — see the docs or source repo.
Use gdal-mcp to clip this GeoTIFF by the provided administrative boundary, and first explain your reasoning for the clipping method, CRS handling, and output format.
A clipped raster file plus a structured justification of the method and key parameter choices.
Use gdal-mcp to reproject this batch of rasters to EPSG:3857 and resample them to 10-meter resolution; before running, explain why you chose that resampling method and its possible data impact.
Processed raster outputs with an explanation of the reprojection, resampling strategy, and potential error impacts.
Use gdal-mcp to analyze this raster’s extent, pixel size, band details, and basic statistics, and first provide a structured explanation of which GDAL/Rasterio operations you will use and why.
A summary of raster metadata and statistics, along with a structured explanation of the analysis steps used.
Let AI control QGIS for spatial analysis, styling, and map export.
Control dynamic maps and layers to visualize geospatial data in AI workflows.
Manage GeoServer REST API tasks and spatial service configuration with natural language.
Control QGIS with an LLM to build projects, load layers, and run geospatial tasks.
Read, inspect, analyze vector geospatial data, and render interactive maps.
Turn unstructured documents into a searchable knowledge base for AI agents.