GIS MCP Server

Instructions

Implementation Reference

• src/gis_mcp/pysal_functions.py:673-777 (handler)

  The knn_weights tool handler: creates k-nearest neighbors spatial weights matrix (W) from geospatial point data using libpysal.weights.KNN, handles ID fields, converts numpy types for JSON serialization, and returns structured summary with previews and stats.
  @gis_mcp.tool() def knn_weights( data_path: str, k: int, id_field: Optional[str] = None ) -> Dict[str, Any]: """ Create a k-nearest neighbors spatial weights (W) object from point data. - data_path: path to point shapefile or GeoPackage - k: number of nearest neighbors - id_field: optional attribute name to use as observation IDs """ try: if not os.path.exists(data_path): return {"status": "error", "message": f"Data file not found: {data_path}"} gdf = gpd.read_file(data_path) if gdf.empty: return {"status": "error", "message": "Input file contains no features"} # Extract coordinates coords = [(geom.x, geom.y) for geom in gdf.geometry] # Create KNN weights import libpysal if id_field and id_field in gdf.columns: ids = gdf[id_field].tolist() w = libpysal.weights.KNN(coords, k=k, ids=ids) else: w = libpysal.weights.KNN(coords, k=k) ids = w.id_order # Convert ids to native Python types immediately ids = [int(i) if isinstance(i, (np.integer, np.int32, np.int64)) else i for i in ids] neighbor_counts = [int(w.cardinalities[i]) if isinstance(w.cardinalities[i], (np.integer, np.int32, np.int64)) else w.cardinalities[i] for i in ids] islands = [int(i) if isinstance(i, (np.integer, np.int32, np.int64)) else i for i in list(w.islands)] if hasattr(w, "islands") else [] # Previews - convert to native Python types immediately preview_ids = ids[:5] neighbors_preview = {} weights_preview = {} for i in preview_ids: # Convert neighbor IDs and weights to native Python types neighbors = w.neighbors.get(i, []) weights_list = w.weights.get(i, []) neighbors_preview[int(i) if isinstance(i, (np.integer, np.int32, np.int64)) else i] = [ int(n) if isinstance(n, (np.integer, np.int32, np.int64, np.int8, np.int16)) else n for n in neighbors ] weights_preview[int(i) if isinstance(i, (np.integer, np.int32, np.int64)) else i] = [ float(w_val) if isinstance(w_val, (np.floating, np.float32, np.float64, np.float16)) else (int(w_val) if isinstance(w_val, (np.integer, np.int32, np.int64, np.int8, np.int16)) else w_val) for w_val in weights_list ] result = { "n": int(w.n), "id_count": int(len(ids)), "k": int(k), "id_field": id_field, "neighbors_stats": { "min": int(min(neighbor_counts)) if neighbor_counts else 0, "max": int(max(neighbor_counts)) if neighbor_counts else 0, "mean": float(np.mean(neighbor_counts)) if neighbor_counts else 0.0, }, "islands": islands, "neighbors_preview": neighbors_preview, "weights_preview": weights_preview, } # Convert numpy types to native Python types for serialization (recursive, final pass) def convert_numpy_types(obj): """Recursively convert numpy types to native Python types.""" if obj is None: return None if isinstance(obj, dict): return {convert_numpy_types(k) if isinstance(k, (np.integer, np.int32, np.int64)) else k: convert_numpy_types(v) for k, v in obj.items()} elif isinstance(obj, (list, tuple)): return [convert_numpy_types(item) for item in obj] elif isinstance(obj, (np.integer, np.int32, np.int64, np.int8, np.int16)): return int(obj) elif isinstance(obj, (np.floating, np.float32, np.float64, np.float16)): return float(obj) elif isinstance(obj, np.ndarray): return obj.tolist() elif hasattr(obj, '__iter__') and not isinstance(obj, (str, bytes)): # Handle other iterable types return [convert_numpy_types(item) for item in obj] else: return obj result = convert_numpy_types(result) return { "status": "success", "message": "KNN spatial weights constructed successfully", "result": result, "weights_info": result, # Also include as weights_info for test compatibility } except Exception as e: logger.error(f"Error creating KNN weights: {str(e)}") return {"status": "error", "message": f"Failed to create KNN weights: {str(e)}"}