fix(mcp): fix pre_validate aliases and sql_expression normalization

Three pre_validate methods were checking only canonical field names, but
the Pydantic schemas accept validation aliases. For example
PieChartConfig.dimension accepts "groupby" as an alias, so sending
{"groupby": ...} would pass the schema but be incorrectly rejected by
pre_validate.

Four normalize_column_refs implementations did not guard against
sql_expression metrics (name=None), causing AttributeError when
_get_canonical_column_name(None, ...) was called. XYChartPlugin already
handled this correctly; the fix brings the other plugins in line.

Pre-validate alias fixes:
- TableChartPlugin: accept columns/all_columns/groupby (AliasChoices)
- PieChartPlugin: accept dimension/groupby (AliasChoices)
- PivotTableChartPlugin: accept rows/groupby/dimension (AliasChoices)

sql_expression normalization guards:
- BigNumberChartPlugin: skip metric normalization when sql_expression set
- PieChartPlugin: same for metric field
- MixedTimeseriesChartPlugin: skip in _norm_list helper
- PivotTableChartPlugin: skip in _norm_col_list helper

superset/mcp_service/chart/plugins/big_number.py

@@ -208,7 +208,9 @@ class BigNumberChartPlugin(BaseChartPlugin):
         config_dict = config.model_dump()
 
         if config_dict.get("metric"):
-            if config_dict["metric"].get("saved_metric"):
+            if config_dict["metric"].get("sql_expression"):
+                pass
+            elif config_dict["metric"].get("saved_metric"):
                 config_dict["metric"]["name"] = (
                     DatasetValidator._get_canonical_metric_name(
                         config_dict["metric"]["name"], dataset_context

superset/mcp_service/chart/plugins/mixed_timeseries.py

@@ -132,6 +132,8 @@ class MixedTimeseriesChartPlugin(BaseChartPlugin):
         def _norm_list(key: str) -> None:
             if config_dict.get(key):
                 for col in config_dict[key]:
+                    if col.get("sql_expression"):
+                        continue
                     if col.get("saved_metric"):
                         col["name"] = DatasetValidator._get_canonical_metric_name(
                             col["name"], dataset_context

superset/mcp_service/chart/plugins/pie.py

@@ -47,7 +47,7 @@ class PieChartPlugin(BaseChartPlugin):
     ) -> ChartGenerationError | None:
         missing_fields = []
 
-        if "dimension" not in config:
+        if "dimension" not in config and "groupby" not in config:
             missing_fields.append("'dimension' (category column for slices)")
         if "metric" not in config:
             missing_fields.append("'metric' (value metric for slice sizes)")
@@ -104,7 +104,9 @@ class PieChartPlugin(BaseChartPlugin):
                 )
             )
         if config_dict.get("metric"):
-            if config_dict["metric"].get("saved_metric"):
+            if config_dict["metric"].get("sql_expression"):
+                pass
+            elif config_dict["metric"].get("saved_metric"):
                 config_dict["metric"]["name"] = (
                     DatasetValidator._get_canonical_metric_name(
                         config_dict["metric"]["name"], dataset_context

superset/mcp_service/chart/plugins/pivot_table.py

@@ -47,7 +47,7 @@ class PivotTableChartPlugin(BaseChartPlugin):
     ) -> ChartGenerationError | None:
         missing_fields = []
 
-        if not config.get("rows"):
+        if not (config.get("rows") or config.get("groupby") or config.get("dimension")):
             missing_fields.append("'rows' (row grouping columns)")
         if not config.get("metrics"):
             missing_fields.append("'metrics' (aggregation metrics)")
@@ -67,7 +67,10 @@ class PivotTableChartPlugin(BaseChartPlugin):
                 error_code="MISSING_PIVOT_FIELDS",
             )
 
-        if not isinstance(config.get("rows", []), list):
+        rows_val = (
+            config.get("rows") or config.get("groupby") or config.get("dimension") or []
+        )
+        if not isinstance(rows_val, list):
             return ChartGenerationError(
                 error_type="invalid_rows_format",
                 message="Rows must be a list of columns",
@@ -123,6 +126,8 @@ class PivotTableChartPlugin(BaseChartPlugin):
         def _norm_col_list(key: str) -> None:
             if config_dict.get(key):
                 for col in config_dict[key]:
+                    if col.get("sql_expression"):
+                        continue
                     if col.get("saved_metric"):
                         col["name"] = DatasetValidator._get_canonical_metric_name(
                             col["name"], dataset_context

superset/mcp_service/chart/plugins/table.py

@@ -46,7 +46,10 @@ class TableChartPlugin(BaseChartPlugin):
         self,
         config: dict[str, Any],
     ) -> ChartGenerationError | None:
-        if not config.get("columns"):
+        columns = (
+            config.get("columns") or config.get("all_columns") or config.get("groupby")
+        )
+        if not columns:
             return ChartGenerationError(
                 error_type="missing_columns",
                 message="Table chart missing required field: columns",
@@ -63,7 +66,7 @@ class TableChartPlugin(BaseChartPlugin):
                 error_code="MISSING_COLUMNS",
             )
 
-        if not isinstance(config.get("columns", []), list):
+        if not isinstance(columns, list):
             return ChartGenerationError(
                 error_type="invalid_columns_format",
                 message="Columns must be a list",