# GIS MCP Server (full context)
Model Context Protocol server that lets AI agents run real GIS workflows
via Shapely, GeoPandas, Rasterio, PyProj, and PySAL. Optional helpers add
admin boundaries, climate, ecology, movement, land cover, and satellite
imagery plus web map visualization.
## Key Features
- Rich GIS toolset: geometry ops, CRS transforms, vector/raster IO, NDVI &
band math, zonal stats, spatial weights & autocorrelation, clustering.
- HTTP/SSE/STDIO transports: run locally in editors or remotely over HTTP/SSE.
- Remote storage API: upload/download files when using HTTP/SSE.
- Extensible: add your own tools or enable extras per workflow.
- AI-friendly: designed to stay open/pinned so editors can feed this context
to MCP-aware agents (Cursor, Claude Desktop, etc.).
## Quick Start (server)
- STDIO (default): `gis-mcp`
- HTTP: `GIS_MCP_TRANSPORT=http GIS_MCP_HOST=0.0.0.0 GIS_MCP_PORT=8080 gis-mcp`
- SSE: `GIS_MCP_TRANSPORT=sse GIS_MCP_HOST=0.0.0.0 GIS_MCP_PORT=8080 gis-mcp`
- Storage path: `GIS_MCP_STORAGE_PATH` or `--storage-path` (default `~/.gis_mcp/data`).
- Debug logs: add `--debug`.
- Endpoints (HTTP/SSE): MCP at `/mcp` or `/sse`, storage at `/storage`.
## Install
- Stable (recommended): `pip install gis-mcp`
- With visualization: `pip install gis-mcp[visualize]`
- With data extras: `pip install gis-mcp[administrative-boundaries]`,
`[climate]`, `[ecology]`, `[movement]`, `[land_cover]`, `[satellite_imagery]`
(combine as needed).
- Docker (HTTP mode on 9010 by default):
- Build: `docker build -t gis-mcp .`
- Run: `docker run -p 9010:9010 gis-mcp`
## Capabilities Overview
- Shapely: buffer, intersection/union/difference, centroid, bounds,
simplify, make_valid, nearest point, distance/area/length.
- PyProj: coordinate transforms, project geometries, EPSG/UTM helpers,
geodetic distance/area.
- GeoPandas: read/write, spatial join/overlay, dissolve, clip, explode,
merge, nearest join, projection helpers.
- Rasterio: read/write GeoTIFF, crop by bbox/geometry, reproject/resample,
mosaic/concat bands, NDVI/band math, hillshade, focal stats, reclassify,
tile, zonal stats.
- PySAL: Moran’s I/Geary’s C/Getis-Ord, local indicators, join counts,
weights builders (KNN/distance), spatial Markov, OLS + spatial diagnostics,
clustering/ADBSCAN.
- Visualization: static map export, interactive web maps (Folium/PyDeck when
visualize extra installed).
- Data helpers (extras): admin boundaries lookup, climate rasters, ecology
data, movement networks, land cover rasters, satellite imagery search/download.
## Use in AI Editors
- Keep this file open or pinned so MCP-aware editors include it as context.
- Add the server command in `.cursor/mcp.json` or Claude config pointing to
`gis-mcp` and desired transport/port.
## Documentation
- Site: https://gis-mcp.com/
- Getting started: https://gis-mcp.com/getting-started/
- Install guides: https://gis-mcp.com/install/
- HTTP transport: https://gis-mcp.com/http-transport/
- GIS AI agent how-to: https://gis-mcp.com/gis-ai-agent/
- API reference (Shapely/PyProj/GeoPandas/Rasterio/PySAL): https://gis-mcp.com/api/
- Examples: https://gis-mcp.com/examples/
- Storage config: https://gis-mcp.com/storage-configuration/
## All Website Pages
- Home: https://gis-mcp.com/
- Getting Started: https://gis-mcp.com/getting-started/
- Install overview: https://gis-mcp.com/install/
- Install - Docker: https://gis-mcp.com/install/docker/
- Install - pip: https://gis-mcp.com/install/pip/
- Install - Developers: https://gis-mcp.com/install/developers/
- Storage Configuration: https://gis-mcp.com/storage-configuration/
- HTTP Transport: https://gis-mcp.com/http-transport/
- GIS AI Agent (overview): https://gis-mcp.com/gis-ai-agent/
- GIS AI Agent - LangChain: https://gis-mcp.com/gis-ai-agent/langchain/
- GIS AI Agent - OpenAI NodeJS: https://gis-mcp.com/gis-ai-agent/openai-nodejs/
- Data Gathering overview: https://gis-mcp.com/data-gathering/
- Administrative Boundaries: https://gis-mcp.com/data-gathering/administrative_boundaries/
- Climate: https://gis-mcp.com/data-gathering/climate/
- Ecology: https://gis-mcp.com/data-gathering/ecology/
- Movement: https://gis-mcp.com/data-gathering/movement/
- Satellite Imagery: https://gis-mcp.com/data-gathering/satellite_imagery/
- Land Cover: https://gis-mcp.com/data-gathering/land_cover/
- Analysis - Shapely overview: https://gis-mcp.com/api/shapely/
- buffer: https://gis-mcp.com/api/shapely/buffer/
- centroid: https://gis-mcp.com/api/shapely/centroid/
- convex hull: https://gis-mcp.com/api/shapely/convex_hull/
- difference: https://gis-mcp.com/api/shapely/difference/
- envelope: https://gis-mcp.com/api/shapely/envelope/
- geojson_to_geometry: https://gis-mcp.com/api/shapely/geojson_to_geometry/
- geometry_to_geojson: https://gis-mcp.com/api/shapely/geometry_to_geojson/
- get_area: https://gis-mcp.com/api/shapely/get_area/
- get_bounds: https://gis-mcp.com/api/shapely/get_bounds/
- get_coordinates: https://gis-mcp.com/api/shapely/get_coordinates/
- get_geometry_type: https://gis-mcp.com/api/shapely/get_geometry_type/
- get_length: https://gis-mcp.com/api/shapely/get_length/
- intersection: https://gis-mcp.com/api/shapely/intersection/
- is_valid: https://gis-mcp.com/api/shapely/is_valid/
- make_valid: https://gis-mcp.com/api/shapely/make_valid/
- minimum_rotated_rectangle: https://gis-mcp.com/api/shapely/minimum_rotated_rectangle/
- nearest_point_on_geometry: https://gis-mcp.com/api/shapely/nearest_point_on_geometry/
- normalize_geometry: https://gis-mcp.com/api/shapely/normalize_geometry/
- rotate_geometry: https://gis-mcp.com/api/shapely/rotate_geometry/
- scale_geometry: https://gis-mcp.com/api/shapely/scale_geometry/
- simplify: https://gis-mcp.com/api/shapely/simplify/
- snap_geometry: https://gis-mcp.com/api/shapely/snap_geometry/
- symmetric_difference: https://gis-mcp.com/api/shapely/symmetric_difference/
- translate_geometry: https://gis-mcp.com/api/shapely/translate_geometry/
- triangulate_geometry: https://gis-mcp.com/api/shapely/triangulate_geometry/
- unary_union_geometries: https://gis-mcp.com/api/shapely/unary_union_geometries/
- union: https://gis-mcp.com/api/shapely/union/
- voronoi: https://gis-mcp.com/api/shapely/voronoi/
- Analysis - PyProj overview: https://gis-mcp.com/api/pyproj/
- transform_coordinates: https://gis-mcp.com/api/pyproj/transform_coordinates/
- project_geometry: https://gis-mcp.com/api/pyproj/project_geometry/
- get_crs_info: https://gis-mcp.com/api/pyproj/get_crs_info/
- get_available_crs: https://gis-mcp.com/api/pyproj/get_available_crs/
- get_utm_zone: https://gis-mcp.com/api/pyproj/get_utm_zone/
- get_utm_crs: https://gis-mcp.com/api/pyproj/get_utm_crs/
- get_geocentric_crs: https://gis-mcp.com/api/pyproj/get_geocentric_crs/
- get_geod_info: https://gis-mcp.com/api/pyproj/get_geod_info/
- calculate_geodetic_distance: https://gis-mcp.com/api/pyproj/calculate_geodetic_distance/
- calculate_geodetic_point: https://gis-mcp.com/api/pyproj/calculate_geodetic_point/
- calculate_geodetic_area: https://gis-mcp.com/api/pyproj/calculate_geodetic_area/
- Analysis - GeoPandas overview: https://gis-mcp.com/api/geopandas/
- append_gpd: https://gis-mcp.com/api/geopandas/append_gpd/
- clip_vector: https://gis-mcp.com/api/geopandas/clip_vector/
- dissolve_gpd: https://gis-mcp.com/api/geopandas/dissolve_gpd/
- explode_gpd: https://gis-mcp.com/api/geopandas/explode_gpd/
- merge_gpd: https://gis-mcp.com/api/geopandas/merge_gpd/
- overlay_gpd: https://gis-mcp.com/api/geopandas/overlay_gpd/
- point_in_polygon: https://gis-mcp.com/api/geopandas/point_in_polygon/
- read_file_gpd: https://gis-mcp.com/api/geopandas/read_file_gpd/
- sjoin_gpd: https://gis-mcp.com/api/geopandas/sjoin_gpd/
- sjoin_nearest_gpd: https://gis-mcp.com/api/geopandas/sjoin_nearest_gpd/
- to_file_gpd: https://gis-mcp.com/api/geopandas/to_file_gpd/
- write_file_gpd: https://gis-mcp.com/api/geopandas/write_file_gpd/
- Analysis - Rasterio overview: https://gis-mcp.com/api/rasterio/
- clip_raster_with_shapefile: https://gis-mcp.com/api/rasterio/clip_raster_with_shapefile/
- compute_ndvi: https://gis-mcp.com/api/rasterio/compute_ndvi/
- concat_bands: https://gis-mcp.com/api/rasterio/concat_bands/
- extract_band: https://gis-mcp.com/api/rasterio/extract_band/
- focal_statistics: https://gis-mcp.com/api/rasterio/focal_statistics/
- get_raster_crs: https://gis-mcp.com/api/rasterio/get_raster_crs/
- hillshade: https://gis-mcp.com/api/rasterio/hillshade/
- metadata_raster: https://gis-mcp.com/api/rasterio/metadata_raster/
- raster_algebra: https://gis-mcp.com/api/rasterio/raster_algebra/
- raster_band_statistics: https://gis-mcp.com/api/rasterio/raster_band_statistics/
- raster_histogram: https://gis-mcp.com/api/rasterio/raster_histogram/
- reclassify_raster: https://gis-mcp.com/api/rasterio/reclassify_raster/
- reproject_raster: https://gis-mcp.com/api/rasterio/reproject_raster/
- resample_raster: https://gis-mcp.com/api/rasterio/resample_raster/
- tile_raster: https://gis-mcp.com/api/rasterio/tile_raster/
- weighted_band_sum: https://gis-mcp.com/api/rasterio/weighted_band_sum/
- write_raster: https://gis-mcp.com/api/rasterio/write_raster/
- zonal_statistics: https://gis-mcp.com/api/rasterio/zonal_statistics/
- Analysis - PySAL overview: https://gis-mcp.com/api/pysal/
- morans_i: https://gis-mcp.com/api/pysal/morans_i/
- gearys_c: https://gis-mcp.com/api/pysal/gearys_c/
- getis_ord_g: https://gis-mcp.com/api/pysal/getis_ord_g/
- moran_local: https://gis-mcp.com/api/pysal/moran_local/
- gamma_statistic: https://gis-mcp.com/api/pysal/gamma_statistic/
- getis_ord_g_local: https://gis-mcp.com/api/pysal/getis_ord_g_local/
- join_counts: https://gis-mcp.com/api/pysal/join_counts/
- join_counts_local: https://gis-mcp.com/api/pysal/join_counts_local/
- adbscan: https://gis-mcp.com/api/pysal/adbscan/
- build_and_transform_weights: https://gis-mcp.com/api/pysal/build_and_transform_weights/
- build_transform_and_save_weights: https://gis-mcp.com/api/pysal/build_transform_and_save_weights/
- distance_band_weights: https://gis-mcp.com/api/pysal/distance_band_weights/
- knn_weights: https://gis-mcp.com/api/pysal/knn_weights/
- ols_with_spatial_diagnostics_safe: https://gis-mcp.com/api/pysal/ols_with_spatial_diagnostics_safe/
- weights_from_shapefile: https://gis-mcp.com/api/pysal/weights_from_shapefile/
- spatial_markov: https://gis-mcp.com/api/pysal/spatial_markov/
- dynamic_lisa: https://gis-mcp.com/api/pysal/dynamic_lisa/
- gm_lag: https://gis-mcp.com/api/pysal/gm_lag/
- Visualization overview: https://gis-mcp.com/api/visualize/
- static map: https://gis-mcp.com/api/visualize/create_map/
- web map: https://gis-mcp.com/api/visualize/create_web_map/
- Examples overview: https://gis-mcp.com/examples/
- Example - basic spatial analysis (Claude): https://gis-mcp.com/examples/basic_spatial_analysis_claude/
- Example - CRS, area & distance: https://gis-mcp.com/examples/crs_area_distance_analysis/
- Contributing: https://gis-mcp.com/contributing/
- Related MCP servers: https://gis-mcp.com/related-mcp-servers/
- Support/Discord: https://discord.gg/dzkXZsZK
