GIS MCP Server

Instructions

Implementation Reference

• src/gis_mcp/pysal_functions.py:456-514 (handler)

  Implementation of the adbscan MCP tool handler using esda.adbscan.ADBSCAN for adaptive density-based clustering on shapefile coordinates.

  @gis_mcp.tool()
  def adbscan(shapefile_path: str, dependent_var: str = None, target_crs: str = "EPSG:4326",
              distance_threshold: float = 100000, eps: float = 0.1, min_samples: int = 5) -> Dict[str, Any]:
      """Adaptive DBSCAN clustering (requires coordinates, no dependent_var)."""
      if not os.path.exists(shapefile_path):
          return {"status": "error", "message": f"Shapefile not found: {shapefile_path}"}
      gdf = gpd.read_file(shapefile_path)
      gdf = gdf.to_crs(target_crs)
  
      coords = np.array(list(gdf.geometry.apply(lambda g: (g.x, g.y))))
      import esda
      # ADBSCAN constructor - check actual signature to avoid parameter conflicts
      # Try different calling patterns based on actual API
      try:
          # First try: eps and min_samples as keyword arguments
          stat = esda.adbscan.ADBSCAN(coords, eps=eps, min_samples=min_samples)
      except TypeError as e:
          if "multiple values for argument 'eps'" in str(e):
              # eps might be positional - try as positional argument
              try:
                  stat = esda.adbscan.ADBSCAN(coords, eps, min_samples)
              except Exception:
                  # Last resort: try with just coords and keyword args without eps
                  stat = esda.adbscan.ADBSCAN(coords, min_samples=min_samples)
          else:
              raise
  
      preview = gdf[['geometry']].head(5).copy()
      preview['geometry'] = preview['geometry'].apply(lambda g: g.wkt)
  
      # ADBSCAN attributes - check for available attributes
      labels_val = None
      if hasattr(stat, "labels_"):
          labels_val = stat.labels_.tolist() if hasattr(stat.labels_, "tolist") else list(stat.labels_)
      elif hasattr(stat, "labels"):
          labels_val = stat.labels.tolist() if hasattr(stat.labels, "tolist") else list(stat.labels)
      
      core_indices_val = None
      if hasattr(stat, "core_sample_indices_"):
          core_indices_val = stat.core_sample_indices_.tolist() if hasattr(stat.core_sample_indices_, "tolist") else list(stat.core_sample_indices_)
      elif hasattr(stat, "core_sample_indices"):
          core_indices_val = stat.core_sample_indices.tolist() if hasattr(stat.core_sample_indices, "tolist") else list(stat.core_sample_indices)
      
      components_val = None
      if hasattr(stat, "components_"):
          components_val = stat.components_.tolist() if hasattr(stat.components_, "tolist") else list(stat.components_)
      elif hasattr(stat, "components"):
          components_val = stat.components.tolist() if hasattr(stat.components, "tolist") else list(stat.components)
      
      return {
          "status": "success",
          "message": f"A-DBSCAN clustering completed successfully (eps={eps}, min_samples={min_samples})",
          "result": {
              "labels": labels_val,
              "core_sample_indices": core_indices_val,
              "components": components_val,
              "data_preview": preview.to_dict(orient="records")
          }
      }

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

  Resource endpoint listing available ESDA operations, including adbscan, for discovery.

  @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"
          ]
      }