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

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Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
(cherry picked from commit 15bab227bb)
This commit is contained in:
Kamil Gabryjelski
2026-03-30 16:38:31 +02:00
committed by Michael S. Molina
parent bda02a3fdc
commit c853d4df63
6 changed files with 323 additions and 30 deletions
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24
superset/mcp_service/chart/chart_utils.py
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@@ -381,8 +381,8 @@ def map_table_config(config: TableChartConfig) -> Dict[str, Any]:
aggregated_metrics = []
for col in config.columns:
if col.aggregate:
# Column has aggregation - treat as metric
if col.is_metric:
# Saved metric or column with aggregation - treat as metric
aggregated_metrics.append(create_metric_object(col))
else:
# No aggregation - treat as raw column
@@ -441,8 +441,16 @@ def map_table_config(config: TableChartConfig) -> Dict[str, Any]:
return form_data
def create_metric_object(col: ColumnRef) -> Dict[str, Any]:
"""Create a metric object for a column with enhanced validation."""
def create_metric_object(col: ColumnRef) -> Dict[str, Any] | str:
"""Create a metric object for a column with enhanced validation.
For saved metrics, returns the metric name as a plain string which
Superset's query engine resolves via its metrics_by_name lookup.
For ad-hoc metrics, returns a SIMPLE expression dict.
"""
if col.saved_metric:
return col.name
# Ensure aggregate is valid - default to SUM if not specified or invalid
valid_aggregates = {
"SUM",
@@ -840,6 +848,8 @@ def _humanize_column(col: ColumnRef) -> str:
if col.label:
return col.label
name = col.name.replace("_", " ").title()
if col.saved_metric:
return name
if col.aggregate:
return f"{col.aggregate.capitalize()}({name})"
return name
@@ -874,9 +884,9 @@ def _truncate(name: str, max_length: int = 60) -> str:
def _table_chart_what(config: TableChartConfig, dataset_name: str | None) -> str:
"""Build the descriptive fragment for a table chart."""
has_agg = any(col.aggregate for col in config.columns)
has_agg = any(col.is_metric for col in config.columns)
if has_agg:
metrics = [col for col in config.columns if col.aggregate]
metrics = [col for col in config.columns if col.is_metric]
what = ", ".join(_humanize_column(m) for m in metrics[:2])
return f"{what} Summary"
if dataset_name:
@@ -1073,7 +1083,7 @@ def analyze_chart_capabilities(chart: Any | None, config: Any) -> ChartCapabilit
# Classify data types
data_types = []
if hasattr(config, "x") and config.x:
data_types.append("categorical" if not config.x.aggregate else "metric")
data_types.append("categorical" if not config.x.is_metric else "metric")
if hasattr(config, "y") and config.y:
data_types.extend(["metric"] * len(config.y))
if "time" in viz_type or "timeseries" in viz_type: