STAC MCP Server

import sys import types from types import SimpleNamespace import numpy as np from stac_mcp.tools.client import STACClient def test_estimate_uses_odc_nbytes(monkeypatch): # Build a fake xarray DataArray-like object class FakeDataArray: def __init__(self, shape, dtype): self._arr = np.zeros(shape, dtype=dtype) @property def data(self): return self._arr @property def shape(self): return self._arr.shape @property def dtype(self): return self._arr.dtype class FakeDataset: def __init__(self, data_vars): self.data_vars = data_vars # Create fake odc.stac module with load function def fake_load(*_args, **_kwargs): # Return a dataset with two variables with known sizes da1 = FakeDataArray((2, 10, 10), np.float32) # nbytes = 2*10*10*4 = 800 da2 = FakeDataArray((2, 5, 5), np.uint16) # nbytes = 2*5*5*2 = 100 return FakeDataset({"B02": da1, "SCL": da2}) odc_mod = types.ModuleType("odc.stac") odc_mod.load = fake_load # Fake xarray module so isinstance check passes xr_mod = types.ModuleType("xarray") xr_mod.Dataset = FakeDataset monkeypatch.setitem(sys.modules, "odc.stac", odc_mod) monkeypatch.setitem(sys.modules, "xarray", xr_mod) # Monkeypatch the client's cached search to return a single fake item fake_item = SimpleNamespace(collection_id="sentinel-2-l2a") def _fake_cached_search(_self, **_kwargs): return [fake_item] monkeypatch.setattr(STACClient, "_cached_search", _fake_cached_search) client = STACClient() res = client.estimate_data_size(collections=["sentinel-2-l2a"], limit=1) # Expected bytes: da1 nbytes + da2 nbytes = 800 + 100 = 900 assert isinstance(res, dict) assert res["item_count"] == 1 expected_total = 900 assert res["estimated_size_bytes"] == expected_total # verify data_variables reported expected_var_count = 2 assert len(res.get("data_variables", [])) == expected_var_count names = {v["variable"] for v in res["data_variables"]} assert names == {"B02", "SCL"}