Files
superset2/superset/mcp_service/chart/plugins/table.py
Amin Ghadersohi 139eea92f6 refactor(mcp): eliminate dead code and complete plugin registry dispatch
H1: Delete 7 dead _pre_validate_* static methods from SchemaValidator
    — exact duplicates of plugin pre_validate() methods, never called
    after _pre_validate_chart_type() was updated to delegate to plugin.

H2: Inline DatasetValidator._normalize_xy_config/_normalize_table_config
    into XYChartPlugin/TableChartPlugin.normalize_column_refs() and delete
    both DatasetValidator helper methods. The 5 other plugins already
    called _get_canonical_column_name directly; XY and Table now match.

H3: Add generate_name()/resolve_viz_type() to ChartTypePlugin protocol
    and BaseChartPlugin, implement in all 7 plugins. Replace the 7-arm
    isinstance chain in generate_chart_name() and the 7-arm elif chain
    in _resolve_viz_type() with single-line registry dispatch.

H4: Add a sync comment above _CHART_TYPE_ERROR_HINTS to document that
    it must stay in sync with the plugin registry.

M4: Move logger=logging.getLogger(__name__) from inside
    XYChartPlugin.get_runtime_warnings() to module scope.
2026-05-07 22:19:03 +00:00

119 lines
4.4 KiB
Python

# Licensed to the Apache Software Foundation (ASF) under one
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# to you under the Apache License, Version 2.0 (the
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"""Table chart type plugin."""
from __future__ import annotations
from typing import Any
from superset.mcp_service.chart.plugin import BaseChartPlugin
from superset.mcp_service.chart.schemas import ColumnRef
from superset.mcp_service.common.error_schemas import ChartGenerationError
class TableChartPlugin(BaseChartPlugin):
"""Plugin for table chart type."""
chart_type = "table"
display_name = "Table"
native_viz_types = {
"table": "Table",
"ag-grid-table": "Interactive Table",
}
def pre_validate(
self,
config: dict[str, Any],
) -> ChartGenerationError | None:
if "columns" not in config:
return ChartGenerationError(
error_type="missing_columns",
message="Table chart missing required field: columns",
details=(
"Table charts require a 'columns' array to specify which "
"columns to display"
),
suggestions=[
"Add 'columns' field with array of column specifications",
"Example: 'columns': [{'name': 'product'}, {'name': 'sales', "
"'aggregate': 'SUM'}]",
"Each column can have optional 'aggregate' for metrics",
],
error_code="MISSING_COLUMNS",
)
if not isinstance(config.get("columns", []), list):
return ChartGenerationError(
error_type="invalid_columns_format",
message="Columns must be a list",
details="The 'columns' field must be an array of column specifications",
suggestions=[
"Ensure columns is an array: 'columns': [...]",
"Each column should be an object with 'name' field",
],
error_code="INVALID_COLUMNS_FORMAT",
)
return None
def extract_column_refs(self, config: Any) -> list[ColumnRef]:
from superset.mcp_service.chart.schemas import TableChartConfig
if not isinstance(config, TableChartConfig):
return []
refs: list[ColumnRef] = list(config.columns)
if config.filters:
for f in config.filters:
refs.append(ColumnRef(name=f.column))
return refs
def to_form_data(
self, config: Any, dataset_id: int | str | None = None
) -> dict[str, Any]:
from superset.mcp_service.chart.chart_utils import map_table_config
return map_table_config(config)
def generate_name(self, config: Any, dataset_name: str | None = None) -> str:
from superset.mcp_service.chart.chart_utils import (
_summarize_filters,
_table_chart_what,
)
what = _table_chart_what(config, dataset_name)
context = _summarize_filters(config.filters)
return f"{what} \u2013 {context}" if context else what
def resolve_viz_type(self, config: Any) -> str:
return getattr(config, "viz_type", "table")
def normalize_column_refs(self, config: Any, dataset_context: Any) -> Any:
from superset.mcp_service.chart.schemas import TableChartConfig
from superset.mcp_service.chart.validation.dataset_validator import (
DatasetValidator,
)
config_dict = config.model_dump()
get_canonical = DatasetValidator._get_canonical_column_name
for col in config_dict.get("columns") or []:
col["name"] = get_canonical(col["name"], dataset_context)
DatasetValidator._normalize_filters(config_dict, dataset_context)
return TableChartConfig.model_validate(config_dict)