GIS MCP Server

Instructions

Implementation Reference

• src/gis_mcp/pysal_functions.py:30-106 (handler)

  The main handler function for the 'getis_ord_g' tool. It loads a shapefile, creates distance-based spatial weights, computes the global Getis-Ord G statistic using esda.G, handles islands, and returns G, p-value, z-score with data preview.

  @gis_mcp.tool()
  def getis_ord_g(
      shapefile_path: str,
      dependent_var: str = "LAND_USE",
      target_crs: str = "EPSG:4326",
      distance_threshold: float = 100000
  ) -> Dict[str, Any]:
      """Compute Getis-Ord G for global hot spot analysis."""
      try:
          # Clean backticks from string parameters
          shapefile_path = shapefile_path.replace("`", "")
          dependent_var = dependent_var.replace("`", "")
          target_crs = target_crs.replace("`", "")
  
          # Validate input file
          if not os.path.exists(shapefile_path):
              logger.error(f"Shapefile not found: {shapefile_path}")
              return {"status": "error", "message": f"Shapefile not found: {shapefile_path}"}
  
          # Load GeoDataFrame
          gdf = gpd.read_file(shapefile_path)
          
          # Validate dependent variable
          if dependent_var not in gdf.columns:
              logger.error(f"Dependent variable '{dependent_var}' not found in columns")
              return {"status": "error", "message": f"Dependent variable '{dependent_var}' not found in shapefile columns"}
  
          # Reproject to target CRS
          gdf = gdf.to_crs(target_crs)
  
          # Convert distance_threshold to degrees if using geographic CRS (e.g., EPSG:4326)
          effective_threshold = distance_threshold
          unit = "meters"
          if target_crs == "EPSG:4326":
              effective_threshold = distance_threshold / 111000
              unit = "degrees"
  
          # Extract dependent data
          dependent = gdf[dependent_var].values.astype(np.float64)
  
          # Create distance-based spatial weights matrix
          import libpysal
          import esda
          w = libpysal.weights.DistanceBand.from_dataframe(gdf, threshold=effective_threshold, binary=False)
          w.transform = 'r'
  
          # Handle islands
          for island in w.islands:
              w.weights[island] = [0] * len(w.weights[island])
              w.cardinalities[island] = 0
  
          # Getis-Ord G
          getis = esda.G(dependent, w)
  
          # Prepare GeoDataFrame preview
          preview = gdf[['geometry', dependent_var]].copy()
          preview['geometry'] = preview['geometry'].apply(lambda g: g.wkt)
          preview = preview.head(5).to_dict(orient="records")
  
          return {
              "status": "success",
              "message": f"Getis-Ord G analysis completed successfully (distance threshold: {effective_threshold} {unit})",
              "result": {
                  "shapefile_path": shapefile_path,
                  "getis_ord_g": {
                      "G": float(getis.G),
                      "p_value": float(getis.p_sim),
                      "z_score": float(getis.z_sim)
                  },
                  "data_preview": preview
              }
          }
      
      except Exception as e:
          logger.error(f"Error performing Getis-Ord G analysis: {str(e)}")
          return {"status": "error", "message": f"Failed to perform Getis-Ord G analysis: {str(e)}"}

• src/gis_mcp/pysal_functions.py:13-28 (registration)

  GIS resource listing available PySAL/ESDA operations, including 'getis_ord_g'. This serves as a discovery mechanism for the tool.

  @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)

  Import of pysal_functions module in main.py, which triggers registration of all @gis_mcp.tool() decorators, including getis_ord_g.

  from . import (
      geopandas_functions,
      shapely_functions,
      rasterio_functions,
      pyproj_functions,
      pysal_functions,
  )