GIS MCP Server

Instructions

Implementation Reference

• src/gis_mcp/pysal_functions.py:187-208 (handler)

  The handler function that executes the gamma_statistic tool, computing the spatial autocorrelation Gamma statistic using PySAL's esda.Gamma on a shapefile with a given dependent variable and spatial weights.
  @gis_mcp.tool() def gamma_statistic(shapefile_path: str, dependent_var: str = "LAND_USE", target_crs: str = "EPSG:4326", distance_threshold: float = 100000) -> Dict[str, Any]: """Compute Gamma Statistic for spatial autocorrelation.""" gdf, y, w, (threshold, unit), err = pysal_load_data(shapefile_path, dependent_var, target_crs, distance_threshold) if err: return {"status": "error", "message": err} import esda stat = esda.Gamma(y, w) preview = gdf[['geometry', dependent_var]].head(5).assign( geometry=lambda df: df.geometry.apply(lambda g: g.wkt) ).to_dict(orient="records") return { "status": "success", "message": f"Gamma Statistic completed successfully (threshold: {threshold} {unit})", "result": { "Gamma": float(stat.gamma), "p_value": float(stat.p_value) if hasattr(stat, "p_value") else None, "data_preview": preview } }
• src/gis_mcp/pysal_functions.py:108-134 (helper)

  Supporting helper function that loads the shapefile, prepares the dependent variable data, creates row-standardized distance-based spatial weights, and handles isolated observations.
  def pysal_load_data(shapefile_path: str, dependent_var: str, target_crs: str, distance_threshold: float): """Common loader and weight creation for esda statistics.""" if not os.path.exists(shapefile_path): return None, None, None, None, f"Shapefile not found: {shapefile_path}" gdf = gpd.read_file(shapefile_path) if dependent_var not in gdf.columns: return None, None, None, None, f"Dependent variable '{dependent_var}' not found in shapefile columns" gdf = gdf.to_crs(target_crs) effective_threshold = distance_threshold unit = "meters" if target_crs.upper() == "EPSG:4326": effective_threshold = distance_threshold / 111000 unit = "degrees" y = gdf[dependent_var].values.astype(np.float64) import libpysal w = libpysal.weights.DistanceBand.from_dataframe(gdf, threshold=effective_threshold, binary=False) w.transform = 'r' for island in w.islands: w.weights[island] = [0] * len(w.weights[island]) w.cardinalities[island] = 0 return gdf, y, w, (effective_threshold, unit), None
• src/gis_mcp/pysal_functions.py:13-29 (registration)

  MCP resource that lists available ESDA spatial analysis operations, including 'gamma_statistic'.
  @gis_mcp.resource("gis://operations/esda") def get_spatial_operations() -> Dict[str, List[str]]: """List available spatial analysis operations. This is for esda library. They are using pysal library.""" return { "operations": [ "getis_ord_g", "morans_i", "gearys_c", "gamma_statistic", "moran_local", "getis_ord_g_local", "join_counts", "join_counts_local", "adbscan" ] }
• src/gis_mcp/main.py:66-72 (registration)

  Imports the pysal_functions module in the main entry point, triggering registration of its @gis_mcp.tool() decorators, including gamma_statistic.
  from . import ( geopandas_functions, shapely_functions, rasterio_functions, pyproj_functions, pysal_functions, )