feat(mcp): support saved metrics from datasets in chart generation (#38955)

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

tests/unit_tests/mcp_service/chart/validation/test_column_name_normalization.py

@@ -679,3 +679,53 @@ class TestNormalizeXAxisFilterConsistency:
         assert normalized.group_by is not None
         assert normalized.filters is not None
         assert normalized.group_by[0].name == normalized.filters[0].column == "AIRLINE"
+
+
+class TestValidateSavedMetrics:
+    """Test that saved_metric refs are validated against dataset metrics."""
+
+    def test_valid_saved_metric_passes(
+        self, mock_dataset_context: DatasetContext
+    ) -> None:
+        config = XYChartConfig(
+            chart_type="xy",
+            x=ColumnRef(name="OrderDate"),
+            y=[ColumnRef(name="TotalRevenue", saved_metric=True)],
+        )
+        is_valid, error = DatasetValidator.validate_against_dataset(
+            config, dataset_id=18, dataset_context=mock_dataset_context
+        )
+        assert is_valid
+        assert error is None
+
+    def test_column_name_as_saved_metric_fails(
+        self, mock_dataset_context: DatasetContext
+    ) -> None:
+        """A regular column marked as saved_metric should be rejected."""
+        config = XYChartConfig(
+            chart_type="xy",
+            x=ColumnRef(name="OrderDate"),
+            y=[ColumnRef(name="Sales", saved_metric=True)],
+        )
+        is_valid, error = DatasetValidator.validate_against_dataset(
+            config, dataset_id=18, dataset_context=mock_dataset_context
+        )
+        assert not is_valid
+        assert error is not None
+        assert error.error_code == "INVALID_SAVED_METRIC"
+
+    def test_nonexistent_saved_metric_fails(
+        self, mock_dataset_context: DatasetContext
+    ) -> None:
+        """A nonexistent saved metric should produce a specific error."""
+        config = XYChartConfig(
+            chart_type="xy",
+            x=ColumnRef(name="OrderDate"),
+            y=[ColumnRef(name="nonexistent_metric", saved_metric=True)],
+        )
+        is_valid, error = DatasetValidator.validate_against_dataset(
+            config, dataset_id=18, dataset_context=mock_dataset_context
+        )
+        assert not is_valid
+        assert error is not None
+        assert error.error_code == "INVALID_SAVED_METRIC"