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" ] }