feat(mcp): chart type plugin registry for extensible generate_chart

[aminghadersohi]

SUMMARY

Introduces a ChartTypePlugin protocol and central registry that consolidates all chart-type-specific logic into a single plugin class per chart type. Previously, adding a new chart type required synchronised edits to four separate dispatch locations. With this change it requires one new file.

The problem: 4 dispatch points that had to stay in sync

Before this PR, generate_chart logic was split across:

Location	Responsibility
schema_validator.py	7 _pre_validate_* static methods, one per type
dataset_validator.py	isinstance branches in _extract_column_references and normalize_column_names
chart_utils.py	if/elif chain in map_config_to_form_data, generate_chart_name, _resolve_viz_type
runtime/__init__.py	isinstance(config, XYChartConfig) hardcoded branch

Result: 5 of 7 chart types (pie, pivot_table, mixed_timeseries, handlebars, big_number) silently skipped column validation — a bug discovered only when charts failed at query time.

---

Architecture

New file layout

superset/mcp_service/chart/
├── plugin.py          # ChartTypePlugin Protocol + BaseChartPlugin base class
├── registry.py        # Central registry with lazy bootstrap
└── plugins/
    ├── __init__.py    # Imports and registers all 7 built-in plugins
    ├── xy.py
    ├── table.py
    ├── pie.py
    ├── pivot_table.py
    ├── mixed_timeseries.py
    ├── handlebars.py
    └── big_number.py

Request flow (before → after)

flowchart LR
    subgraph BEFORE["Before: 4 separate dispatch points"]
        direction TB
        A1[schema_validator.py\n7 pre_validate methods]
        A2[dataset_validator.py\nisinstance branches]
        A3[chart_utils.py\nif/elif chains]
        A4[runtime/__init__.py\nXY hardcoding]
    end

    subgraph AFTER["After: single registry lookup"]
        direction TB
        B1[registry.get chart_type] --> B2[plugin.pre_validate]
        B1 --> B3[plugin.extract_column_refs]
        B1 --> B4[plugin.to_form_data]
        B1 --> B5[plugin.post_map_validate]
        B1 --> B6[plugin.normalize_column_refs]
        B1 --> B7[plugin.get_runtime_warnings]
        B1 --> B8[plugin.generate_name]
        B1 --> B9[plugin.resolve_viz_type]
    end

    BEFORE -.->|refactored to| AFTER

Loading

Plugin protocol

classDiagram
    class ChartTypePlugin {
        <<Protocol>>
        +str chart_type
        +str display_name
        +dict native_viz_types
        +pre_validate(config) ChartGenerationError|None
        +extract_column_refs(config) list[ColumnRef]
        +to_form_data(config, dataset_id) dict
        +post_map_validate(config, form_data, dataset_id) ChartGenerationError|None
        +normalize_column_refs(config, dataset_context) Any
        +get_runtime_warnings(config, dataset_id) list[str]
        +generate_name(config, dataset_name) str
        +resolve_viz_type(config) str
    }

    class BaseChartPlugin {
        +pre_validate() None
        +extract_column_refs() []
        +to_form_data() NotImplementedError
        +post_map_validate() None
        +normalize_column_refs() config unchanged
        +get_runtime_warnings() []
        +generate_name() "Chart"
        +resolve_viz_type() "unknown"
        +_with_context(what, context)$ str
    }

    class XYChartPlugin
    class PieChartPlugin
    class TableChartPlugin
    class BigNumberChartPlugin
    class PivotTableChartPlugin
    class MixedTimeseriesChartPlugin
    class HandlebarsChartPlugin

    ChartTypePlugin <|.. BaseChartPlugin
    BaseChartPlugin <|-- XYChartPlugin
    BaseChartPlugin <|-- PieChartPlugin
    BaseChartPlugin <|-- TableChartPlugin
    BaseChartPlugin <|-- BigNumberChartPlugin
    BaseChartPlugin <|-- PivotTableChartPlugin
    BaseChartPlugin <|-- MixedTimeseriesChartPlugin
    BaseChartPlugin <|-- HandlebarsChartPlugin

Loading

Registry bootstrap (lazy, no circular imports)

sequenceDiagram
    participant Caller as Caller (test / tool)
    participant Registry as registry.py
    participant Plugins as plugins/__init__.py

    Caller->>Registry: get("xy")
    Registry->>Registry: _ensure_plugins_loaded()
    alt first call
        Registry->>Plugins: import (triggers register() calls)
        Plugins-->>Registry: 7 plugins registered
    end
    Registry-->>Caller: XYChartPlugin instance

Loading

The registry self-bootstraps on first lookup — no dependency on app.py being imported first, so isolated unit tests work without the full Flask app.

---

How to add a new chart type

Everything goes in one new file. Here is a minimal working example for a hypothetical funnel chart:

Step 1 — Add the Pydantic schema (chart/schemas.py):

class FunnelChartConfig(ChartConfig):
    chart_type: Literal["funnel"] = "funnel"
    metric: ColumnRef
    groupby: ColumnRef
    filters: list[FilterConfig] | None = None

Step 2 — Create the plugin (chart/plugins/funnel.py):

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 FunnelChartPlugin(BaseChartPlugin):
    chart_type = "funnel"
    display_name = "Funnel Chart"
    native_viz_types = {"funnel": "Funnel Chart"}

    def pre_validate(self, config: dict) -> ChartGenerationError | None:
        if "metric" not in config or "groupby" not in config:
            return ChartGenerationError(
                error_type="missing_funnel_fields",
                message="Funnel chart requires 'metric' and 'groupby'",
                suggestions=["Add metric: {'name': 'col', 'aggregate': 'SUM'}"],
                error_code="MISSING_FUNNEL_FIELDS",
            )
        return None

    def extract_column_refs(self, config) -> list[ColumnRef]:
        from superset.mcp_service.chart.schemas import FunnelChartConfig
        if not isinstance(config, FunnelChartConfig):
            return []
        refs = [config.metric, config.groupby]
        if config.filters:
            refs.extend(ColumnRef(name=f.column) for f in config.filters)
        return refs

    def to_form_data(self, config, dataset_id=None) -> dict:
        return {
            "viz_type": "funnel",
            "metric": {"column": {"column_name": config.metric.name},
                       "aggregate": config.metric.aggregate},
            "groupby": [config.groupby.name],
        }

    def resolve_viz_type(self, config) -> str:
        return "funnel"

    def generate_name(self, config, dataset_name=None) -> str:
        return self._with_context(
            f"Funnel: {config.metric.aggregate}({config.metric.name})",
            dataset_name,
        )

    def normalize_column_refs(self, config, dataset_context):
        from superset.mcp_service.chart.schemas import FunnelChartConfig
        from superset.mcp_service.chart.validation.dataset_validator import DatasetValidator
        d = config.model_dump()
        get = DatasetValidator._get_canonical_column_name
        d["metric"]["name"] = get(d["metric"]["name"], dataset_context)
        d["groupby"]["name"] = get(d["groupby"]["name"], dataset_context)
        DatasetValidator._normalize_filters(d, dataset_context)
        return FunnelChartConfig.model_validate(d)

Step 3 — Register it (chart/plugins/__init__.py):

from superset.mcp_service.chart.plugins.funnel import FunnelChartPlugin
# ...existing imports...
register(FunnelChartPlugin())

Step 4 — Add a Pydantic error hint (chart/validation/schema_validator.py):

# In _CHART_TYPE_ERROR_HINTS — keep in sync with plugins/
"funnel": {
    "metric": "Add metric: {'name': 'col', 'aggregate': 'SUM'}",
    "groupby": "Add groupby: {'name': 'stage_column'}",
},

That is the complete change set — no edits to chart_utils.py, dataset_validator.py, or runtime/__init__.py dispatch logic.

---

Bug fixes included

• 5-type column validation gap: pie, pivot_table, mixed_timeseries, handlebars, and big_number charts previously skipped dataset column validation entirely (silent false-pass). All 7 types now run column existence and aggregation checks.
• BigNumber trendline check: Moved out of the monolithic dispatcher into BigNumberChartPlugin.post_map_validate().
• Runtime validator decoupling: Removed isinstance(config, XYChartConfig) hardcoding from RuntimeValidator; XY-specific format/cardinality checks now live in XYChartPlugin.get_runtime_warnings().
• Stale docstring: generate_chart.py listed only 'xy' and 'table' as valid chart types; updated to list all 7.
• Pydantic error handling: Added missing pie, pivot_table, mixed_timeseries cases to _enhance_validation_error; refactored to a data-driven lookup table to reduce cyclomatic complexity.
• Registry bootstrap: Registry self-bootstraps on first access so unit tests running without app.py get a fully populated registry.

BEFORE/AFTER SCREENSHOTS OR ANIMATED GIF

N/A — backend-only change, no UI modifications.

TESTING INSTRUCTIONS

# Run the MCP unit tests for chart functionality
pytest tests/unit_tests/mcp_service/chart/ -x -q

# Spot-check column validation now works for previously-unvalidated types
pytest tests/unit_tests/mcp_service/chart/validation/ -x -q

Manual verification:

1. Start the MCP service: python -m superset.mcp_service.server
2. Create a pie chart with a non-existent dimension column → should get a column_not_found error with fuzzy suggestions (previously: silent pass, runtime failure)
3. Create a big_number chart with show_trendline=true but no temporal_column → should error at validation
4. Run generate_chart for all 7 chart types to confirm no regressions

ADDITIONAL INFORMATION

• Has associated issue:
• Required feature flags:
• Changes UI
• Includes DB Migration
• Introduces new feature or API
• Removes existing feature or API

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[bito-code-review]

The inline context is insufficient to determine what 'this' refers to in the question. No diff hunk or suggestion snippet is provided for the thread in superset/mcp_service/chart/chart_utils.py.

[Copilot]

Pull request overview

This PR introduces a chart-type plugin architecture for the MCP chart pipeline, centralizing per-chart-type behavior (pre-validation, column extraction/normalization, form_data mapping, runtime warnings, naming, viz_type resolution) into one plugin per supported chart_type. This reduces the previous need to update multiple dispatch sites when adding or modifying chart types.

Changes:

• Added a ChartTypePlugin protocol + BaseChartPlugin, a global plugin registry, and 7 built-in chart plugins (xy, table, pie, pivot_table, mixed_timeseries, handlebars, big_number).
• Refactored schema/dataset/runtime validation and form_data mapping to dispatch via the registry instead of isinstance / if/elif chains.
• Enhanced tool/docs output by expanding generate_chart docstring chart types and adding chart_type_display_name to ChartInfo.

Reviewed changes

Copilot reviewed 17 out of 17 changed files in this pull request and generated 9 comments.

Show a summary per file

File	Description
superset/mcp_service/chart/validation/schema_validator.py	Switch pre-validation dispatch to registry/plugins; refactor Pydantic error enhancement.
superset/mcp_service/chart/validation/runtime/init.py	Replace XY-only runtime checks with per-plugin runtime warning dispatch.
superset/mcp_service/chart/validation/dataset_validator.py	Delegate column extraction/normalization to plugins; apply aggregation validation for all chart types.
superset/mcp_service/chart/tool/generate_chart.py	Update tool docstring to list all supported chart_type values.
superset/mcp_service/chart/schemas.py	Add chart_type_display_name to ChartInfo; resolve display name via registry mapping.
superset/mcp_service/chart/registry.py	Introduce global registry for chart plugins + viz_type display-name lookup.
superset/mcp_service/chart/plugin.py	Add plugin protocol and base class defining the plugin responsibilities.
superset/mcp_service/chart/plugins/init.py	Register the built-in plugins on import.
superset/mcp_service/chart/plugins/xy.py	Implement XY plugin (pre-validate, refs, mapping, naming, viz_type, runtime warnings).
superset/mcp_service/chart/plugins/table.py	Implement Table plugin.
superset/mcp_service/chart/plugins/pie.py	Implement Pie plugin.
superset/mcp_service/chart/plugins/pivot_table.py	Implement Pivot Table plugin.
superset/mcp_service/chart/plugins/mixed_timeseries.py	Implement Mixed Timeseries plugin.
superset/mcp_service/chart/plugins/handlebars.py	Implement Handlebars plugin.
superset/mcp_service/chart/plugins/big_number.py	Implement Big Number plugin including post-map trendline temporal validation.
superset/mcp_service/chart/chart_utils.py	Replace mapping/name/viz_type dispatch with registry/plugin calls.
superset/mcp_service/app.py	Import plugins at MCP app startup; update default instructions about chart type naming.

[codecov]

Codecov Report

❌ Patch coverage is 34.33476% with 459 lines in your changes missing coverage. Please review.
✅ Project coverage is 64.12%. Comparing base (a183582) to head (42a293a).
⚠️ Report is 46 commits behind head on master.

Files with missing lines	Patch %	Lines
superset/mcp_service/chart/plugins/xy.py	28.75%	57 Missing ⚠️
...rset/mcp_service/chart/plugins/mixed_timeseries.py	26.86%	49 Missing ⚠️
superset/mcp_service/chart/registry.py	42.35%	44 Missing and 5 partials ⚠️
superset/mcp_service/chart/plugins/big_number.py	28.78%	47 Missing ⚠️
superset/mcp_service/chart/plugins/handlebars.py	29.03%	44 Missing ⚠️
superset/mcp_service/chart/plugins/pivot_table.py	31.03%	40 Missing ⚠️
superset/mcp_service/chart/plugins/pie.py	37.50%	30 Missing ⚠️
superset/mcp_service/chart/plugins/table.py	40.00%	27 Missing ⚠️
.../mcp_service/chart/validation/dataset_validator.py	14.81%	23 Missing ⚠️
superset/mcp_service/chart/plugin.py	64.70%	18 Missing ⚠️
... and 6 more

Additional details and impacted files

@@            Coverage Diff             @@
##           master   #39922      +/-   ##
==========================================
- Coverage   64.18%   64.12%   -0.07%     
==========================================
  Files        2591     2602      +11     
  Lines      138471   139388     +917     
  Branches    32120    32254     +134     
==========================================
+ Hits        88883    89381     +498     
- Misses      48056    48469     +413     
- Partials     1532     1538       +6     

Flag	Coverage Δ
hive	39.47% <34.33%> (+0.06%)	⬆️
mysql	58.84% <34.33%> (-0.21%)	⬇️
postgres	58.92% <34.33%> (-0.21%)	⬇️
presto	41.13% <34.33%> (+0.04%)	⬆️
python	60.47% <34.33%> (-0.10%)	⬇️
sqlite	58.57% <34.33%> (-0.21%)	⬇️
unit	100.00% <ø> (ø)

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[aminghadersohi]

Internal architecture review — findings & resolutions

Ran an independent codex review of the plugin registry design. Summary of findings and their status:

Finding	Severity	Status
_ensure_plugins_loaded() flag not lock-protected — race condition on concurrent first-calls	MEDIUM	✅ Fixed in eface3b — added threading.Lock with double-checked locking
Duplicate register() call silently overwrites	MEDIUM	Already handled — registry.py lines 68-71 emit logger.warning on overwrite, no silent overwrite
extract_column_refs() returns [] for unrecognised configs	MEDIUM	Accepted — correct for the 7 known types (plugins always registered); unknown types have no column schema to validate against
BaseChartPlugin.extract_column_refs() no-op default	NIT	Accepted — base class provides a safe default; concrete plugins override for their types
Local imports in chart_utils.py / dataset_validator.py	NIT	Already addressed in previous commit with explanatory comments on why they must stay local (circular dependency prevention)

The thread-safety fix (eface3bf) is the only code change — the others are either already handled or accepted design decisions.

[codeant-ai-for-open-source]

Suggestion: Saved metrics are skipped during name normalization, so case-insensitive validation can pass but the emitted metric string can still use the wrong casing and fail later in Superset's exact-name metric lookup. Normalize saved metric names against dataset metrics (not just raw columns) so the final form_data always uses canonical metric names. [api mismatch]

Severity Level: Critical 🚨

- ❌ Big Number charts with mis-cased saved metrics fail.
- ⚠️ Validation passes but Superset query errors at runtime.
- ⚠️ LLM clients see confusing success-then-fail behavior.

Steps of Reproduction ✅

1. Define a dataset in Superset with a saved metric whose name differs in case from the
desired client input, e.g. metric_name="Revenue" (saved metric) on a dataset that
ValidationPipeline will load via DatasetValidator._get_dataset_context at
`superset/mcp_service/chart/validation/dataset_validator.py:197-252`.

2. Call the MCP tool `generate_chart` at
`superset/mcp_service/chart/tool/generate_chart.py:95` with a Big Number config using the
saved metric but with different casing, e.g. `"metric": {"name": "revenue",
"saved_metric": true}` in `GenerateChartRequest.config` (chart_type="big_number").

3. ValidationPipeline.validate_request_with_warnings at
`superset/mcp_service/chart/validation/pipeline.py:90-55` invokes
DatasetValidator.validate_against_dataset at `dataset_validator.py:55-72`, which calls
`_validate_saved_metrics` at `dataset_validator.py:485-46`; this checks
`col_ref.name.lower()` against `available_metrics` lowercased, so `"revenue"` is accepted
as a valid saved metric even though its canonical name is `"Revenue"`.

4. The same pipeline then normalizes column names via
DatasetValidator.normalize_column_names at `dataset_validator.py:94-141`, which delegates
to BigNumberChartPlugin.normalize_column_refs at
`superset/mcp_service/chart/plugins/big_number.py:189-203`; because the metric has
`"saved_metric": true`, the guard at lines 192-195 skips `_get_canonical_column_name`, so
the metric name remains `"revenue"` and is never canonicalized to `"Revenue"`.

5. After validation, `generate_chart` calls `map_config_to_form_data` at
`superset/mcp_service/chart/chart_utils.py:93-131`, which routes to
BigNumberChartPlugin.to_form_data at `big_number.py:132-135`; `map_big_number_config` at
`chart_utils.py:763-799` builds form_data using `create_metric_object(config.metric)` at
`chart_utils.py:11-51`, which returns the raw `col.name` string `"revenue"` for saved
metrics.

6. When Superset executes the query, its SQLA connector resolves metrics via a
`metrics_by_name` dict (see docstring and usage at `chart_utils.py:491-499` and
`superset/connectors/sqla/models.py:1758-38`); because the dict keys are the canonical
metric names (e.g. `"Revenue"`), the mis-cased `"revenue"` key is missing and Superset
raises `QueryObjectValidationError("Metric '%(metric)s' does not exist")`, causing the Big
Number chart generation to fail at runtime even though validation passed.

Fix in Cursor | Fix in VSCode Claude

(Use Cmd/Ctrl + Click for best experience)

Prompt for AI Agent 🤖

This is a comment left during a code review.

**Path:** superset/mcp_service/chart/plugins/big_number.py
**Line:** 192:195
**Comment:**
	*Api Mismatch: Saved metrics are skipped during name normalization, so case-insensitive validation can pass but the emitted metric string can still use the wrong casing and fail later in Superset's exact-name metric lookup. Normalize saved metric names against dataset metrics (not just raw columns) so the final form_data always uses canonical metric names.

Validate the correctness of the flagged issue. If correct, How can I resolve this? If you propose a fix, implement it and please make it concise.
Once fix is implemented, also check other comments on the same PR, and ask user if the user wants to fix the rest of the comments as well. if said yes, then fetch all the comments validate the correctness and implement a minimal fix

👍 | 👎

[aminghadersohi]

The normalization is already in place. Both big_number.py and pie.py branch on saved_metric in normalize_column_refs: when saved_metric=True, the name is resolved through DatasetValidator._get_canonical_metric_name(), which performs a case-insensitive lookup exclusively against available_metrics and returns the canonical dataset name. Non-saved metrics go through _get_canonical_column_name instead. The described bug doesn't exist in the current code.

[codeant-ai-for-open-source]

Suggestion: Metrics are normalized with a helper that prefers matching dataset columns before metrics, so a saved metric that case-insensitively collides with a column name can be rewritten to the column identifier and stop resolving as a saved metric. Handle metric normalization with metric-aware precedence (or branch on saved_metric) to preserve metric identity. [logic error]

Severity Level: Critical 🚨

- ❌ Pivot table charts can fail on valid saved metrics.
- ⚠️ Column-first canonicalization corrupts saved metric identity.
- ⚠️ Users get runtime metric-not-found errors after validation.

Steps of Reproduction ✅

1. Consider a dataset where a physical column and a saved metric share the same
case-insensitive name but differ in canonical casing, e.g. available_columns includes
{"name": "Revenue"} and available_metrics includes {"name": "REVENUE"} as built in
DatasetValidator._get_dataset_context at
`superset/mcp_service/chart/validation/dataset_validator.py:197-252`.

2. A client calls the MCP `generate_chart` tool at
`superset/mcp_service/chart/tool/generate_chart.py:95` with a pivot_table config
referencing the saved metric using a lowercased name, e.g. `"metrics": [{"name":
"revenue", "saved_metric": true}]` in `PivotTableChartConfig`
(`superset/mcp_service/chart/schemas.py:243-261`).

3. ValidationPipeline.validate_request_with_warnings at
`superset/mcp_service/chart/validation/pipeline.py:90-55` runs
DatasetValidator.validate_against_dataset at `dataset_validator.py:55-72`, which calls
`_validate_saved_metrics` at `dataset_validator.py:485-46`; this checks
`col_ref.name.lower()` against `available_metrics` lowercased, so `"revenue"` is accepted
as a valid saved metric in spite of the ambiguous column name.

4. In the column-normalization layer, ValidationPipeline._normalize_column_names at
`validation/pipeline.py:137-181` calls DatasetValidator.normalize_column_names at
`dataset_validator.py:94-141`, which looks up the PivotTableChartPlugin and invokes its
normalize_column_refs at `superset/mcp_service/chart/plugins/pivot_table.py:120-134`;
`_norm_col_list("metrics")` at lines 123-131 runs `_get_canonical_column_name` at
`dataset_validator.py:46-77` for each metric, and because that helper checks
`available_columns` before `available_metrics`, the shared lowercase name `"revenue"` is
canonicalized to the column's name `"Revenue"` instead of the saved metric's canonical
name `"REVENUE"`.

5. The now-normalized config is used in `generate_chart` to build Superset form_data via
map_config_to_form_data at `chart_utils.py:93-131`, which routes to
PivotTableChartPlugin.to_form_data at `pivot_table.py:107-110`; `map_pivot_table_config`
at `superset/mcp_service/chart/chart_utils.py:841-871` builds the `"metrics"` list by
calling `create_metric_object(col)` at `chart_utils.py:11-51`, which returns the string
`"Revenue"` for the saved metric because `col.saved_metric` is still True.

6. At query execution time, Superset resolves metric strings via `metrics_by_name` (see
docstring at `chart_utils.py:491-499` and access pattern at
`superset/connectors/sqla/models.py:1758-38`); this dict is keyed by the canonical saved
metric name `"REVENUE"`, so the rewritten `"Revenue"` key is missing, causing
`QueryObjectValidationError("Metric '%(metric)s' does not exist")` and breaking pivot
table chart execution for datasets where saved metric names collide case-insensitively
with column names.

Fix in Cursor | Fix in VSCode Claude

(Use Cmd/Ctrl + Click for best experience)

Prompt for AI Agent 🤖

This is a comment left during a code review.

**Path:** superset/mcp_service/chart/plugins/pivot_table.py
**Line:** 123:132
**Comment:**
	*Logic Error: Metrics are normalized with a helper that prefers matching dataset columns before metrics, so a saved metric that case-insensitively collides with a column name can be rewritten to the column identifier and stop resolving as a saved metric. Handle metric normalization with metric-aware precedence (or branch on `saved_metric`) to preserve metric identity.

Validate the correctness of the flagged issue. If correct, How can I resolve this? If you propose a fix, implement it and please make it concise.
Once fix is implemented, also check other comments on the same PR, and ask user if the user wants to fix the rest of the comments as well. if said yes, then fetch all the comments validate the correctness and implement a minimal fix

👍 | 👎

[codeant-ai-for-open-source]

Suggestion: The pre-validation only checks for x, y, and y_secondary keys, but the schema explicitly accepts aliases (x_axis, metrics, metrics_b). Valid payloads that use those aliases will be rejected before Pydantic parsing, causing false validation failures. Make pre-validation honor the same aliases as the schema. [api mismatch]

Severity Level: Major ⚠️

- ❌ SchemaValidator rejects mixed_timeseries configs using alias keys.
- ⚠️ Direct callers cannot rely on x_axis/metrics/metrics_b aliases.

Steps of Reproduction ✅

1. Use the schema-level entrypoint `SchemaValidator.validate_request` at
`superset/mcp_service/chart/validation/schema_validator.py:40-57` with a raw request dict
shaped like:

   `{"dataset_id": 1, "config": {"chart_type": "mixed_timeseries", "x_axis": {"name":
   "ds"}, "metrics": [{"name": "revenue", "aggregate": "SUM"}], "metrics_b": [{"name":
   "orders", "aggregate": "COUNT"}]}}`.

2. Inside `_pre_validate` at `schema_validator.py:64-143`, the function extracts `config =
data["config"]` and dispatches to `_pre_validate_chart_type(chart_type, config)` at
`schema_validator.py:145-173`, passing this raw config dict (still using the alias keys
`x_axis`, `metrics`, and `metrics_b`).

3. `_pre_validate_chart_type` looks up the plugin from the registry and calls
`plugin.pre_validate(config)` at `schema_validator.py:190-201`; for
`chart_type="mixed_timeseries"` this resolves to `MixedTimeseriesChartPlugin.pre_validate`
defined in `superset/mcp_service/chart/plugins/mixed_timeseries.py:44-55`.

4. `MixedTimeseriesChartPlugin.pre_validate` only checks for `"x"`, `"y"`, and
`"y_secondary"` keys (lines 50-55) and does not recognize the schema-supported aliases
`x_axis`, `metrics`, and `metrics_b` defined on `MixedTimeseriesChartConfig` in
`superset/mcp_service/chart/schemas.py:18-51`, so `missing_fields` becomes non-empty, a
`ChartGenerationError` is returned, and `SchemaValidator.validate_request` exits early at
line 51, rejecting a payload that would successfully parse via Pydantic if alias keys were
honored.

Fix in Cursor | Fix in VSCode Claude

(Use Cmd/Ctrl + Click for best experience)

Prompt for AI Agent 🤖

This is a comment left during a code review.

**Path:** superset/mcp_service/chart/plugins/mixed_timeseries.py
**Line:** 50:55
**Comment:**
	*Api Mismatch: The pre-validation only checks for `x`, `y`, and `y_secondary` keys, but the schema explicitly accepts aliases (`x_axis`, `metrics`, `metrics_b`). Valid payloads that use those aliases will be rejected before Pydantic parsing, causing false validation failures. Make pre-validation honor the same aliases as the schema.

Validate the correctness of the flagged issue. If correct, How can I resolve this? If you propose a fix, implement it and please make it concise.
Once fix is implemented, also check other comments on the same PR, and ask user if the user wants to fix the rest of the comments as well. if said yes, then fetch all the comments validate the correctness and implement a minimal fix

👍 | 👎

[codeant-ai-for-open-source]

Suggestion: Metric name normalization rewrites every entry in y/y_secondary without checking saved_metric, so saved metrics can be incorrectly remapped to similarly named dataset columns (column lookup is prioritized), breaking saved-metric resolution later. Skip canonical-column normalization for items marked as saved_metric. [logic error]

Severity Level: Critical 🚨

- ❌ Mixed_timeseries charts using saved metrics may fail at runtime.
- ⚠️ Saved metrics shadowed by columns resolve to wrong identifiers.

Steps of Reproduction ✅

1. Call the MCP tool `generate_chart` defined in
`superset/mcp_service/chart/tool/generate_chart.py:95-132` with a `GenerateChartRequest`
whose `config.chart_type` is `"mixed_timeseries"` and whose `y`/`y_secondary` lists
contain saved metrics, e.g. `{"name": "count", "saved_metric": true}` as described by
`ColumnRef.saved_metric` in `superset/mcp_service/chart/schemas.py:27-58`.

2. Inside `generate_chart`, the code invokes
`ValidationPipeline.validate_request_with_warnings(request.model_dump())` at
`generate_chart.py:205-210`; in `ValidationPipeline.validate_request_with_warnings`
(`superset/mcp_service/chart/validation/pipeline.py:90-145`), after schema and dataset
validation, it calls `_normalize_column_names` at `pipeline.py:227-260`.

3. `_normalize_column_names` delegates to `DatasetValidator.normalize_column_names(config,
dataset_id, dataset_context)` at
`superset/mcp_service/chart/validation/dataset_validator.py:351-398`; this looks up the
`mixed_timeseries` plugin from the registry and calls
`plugin.normalize_column_refs(config, dataset_context)`, which is
`MixedTimeseriesChartPlugin.normalize_column_refs` at
`superset/mcp_service/chart/plugins/mixed_timeseries.py:84-106`.

4. In `normalize_column_refs`, helper `_norm_list` (lines 93-98) iterates over every entry
in `y` and `y_secondary` and unconditionally rewrites `col["name"]` using
`DatasetValidator._get_canonical_column_name(col["name"], dataset_context)`, ignoring
`col.get("saved_metric")`; `_get_canonical_column_name` at `dataset_validator.py:303-334`
searches `dataset_context.available_columns` first and only then `available_metrics`, so
in a dataset that defines both a column `"COUNT"` and a saved metric `"count"`, a
saved-metric reference `{"name": "count", "saved_metric": true}` in `y` is normalized to
`"COUNT"`. Later, `map_mixed_timeseries_config` in
`superset/mcp_service/chart/chart_utils.py:21-46` calls `create_metric_object` on these
`ColumnRef` objects; for saved metrics it returns `col.name` (now `"COUNT"`) as the metric
identifier. Because Superset's metric lookup expects the original metric name (e.g.
`"count"`), the chart's metrics no longer resolve correctly, causing mixed_timeseries
charts using such saved metrics to fail or behave incorrectly at query time.

Fix in Cursor | Fix in VSCode Claude

(Use Cmd/Ctrl + Click for best experience)

Prompt for AI Agent 🤖

This is a comment left during a code review.

**Path:** superset/mcp_service/chart/plugins/mixed_timeseries.py
**Line:** 93:98
**Comment:**
	*Logic Error: Metric name normalization rewrites every entry in `y`/`y_secondary` without checking `saved_metric`, so saved metrics can be incorrectly remapped to similarly named dataset columns (column lookup is prioritized), breaking saved-metric resolution later. Skip canonical-column normalization for items marked as `saved_metric`.

Validate the correctness of the flagged issue. If correct, How can I resolve this? If you propose a fix, implement it and please make it concise.
Once fix is implemented, also check other comments on the same PR, and ask user if the user wants to fix the rest of the comments as well. if said yes, then fetch all the comments validate the correctness and implement a minimal fix

👍 | 👎

[codeant-ai-for-open-source]

Suggestion: This adds a full dataset validation call before validate_and_compile, but validate_and_compile already performs the same dataset validation internally using the already-loaded dataset object. The extra call triggers redundant dataset context fetching and duplicate validation work on every update request, increasing latency and database load. [performance]

Severity Level: Major ⚠️

- ⚠️ update_chart validation calls DatasetValidator twice per request.
- ⚠️ Extra dataset queries increase chart-update latency and load.

Steps of Reproduction ✅

1. Call the MCP `update_chart` tool defined at
`superset/mcp_service/chart/tool/update_chart.py:304-595` with `generate_preview=False`
and a non-null `config` so that `parsed_config` is set (`update_chart.py:413-415`) and a
full visualization update runs.

2. After building the update payload via `_build_update_payload`
(`update_chart.py:87-121`), `update_chart` extracts `new_form_data` and, before
persisting, invokes `_validate_update_against_dataset(parsed_config, new_form_data,
chart)` inside the `mcp.update_chart.validation` log context at `update_chart.py:450-453`.

3. Inside `_validate_update_against_dataset` (`update_chart.py:167-251`), once the dataset
ORM object is resolved (`dataset` from `DatasetDAO.find_by_id` at 178-180), the new code
block at lines 199-205 calls `DatasetValidator.validate_against_dataset(parsed_config,
dataset.id)` without passing a `dataset_context`, causing `DatasetValidator` to fetch
dataset context from the database via `_get_dataset_context`
(`superset/mcp_service/chart/validation/dataset_validator.py:197-253`) and run full column
and aggregation validation once.

4. Immediately afterwards, `_validate_update_against_dataset` calls
`validate_and_compile(parsed_config, form_data, dataset, run_compile_check=True)` at
`update_chart.py:222-224`, and `validate_and_compile` in
`superset/mcp_service/chart/compile.py:19-84` builds a new `dataset_context` from the same
ORM dataset and calls `DatasetValidator.validate_against_dataset(config, dataset.id,
dataset_context=dataset_context)` again (compile.py:48-52), resulting in two complete
Tier-1 dataset validation passes and two dataset-context constructions for every
update_chart request.

Fix in Cursor | Fix in VSCode Claude

(Use Cmd/Ctrl + Click for best experience)

Prompt for AI Agent 🤖

This is a comment left during a code review.

**Path:** superset/mcp_service/chart/tool/update_chart.py
**Line:** 203:220
**Comment:**
	*Performance: This adds a full dataset validation call before `validate_and_compile`, but `validate_and_compile` already performs the same dataset validation internally using the already-loaded dataset object. The extra call triggers redundant dataset context fetching and duplicate validation work on every update request, increasing latency and database load.

Validate the correctness of the flagged issue. If correct, How can I resolve this? If you propose a fix, implement it and please make it concise.
Once fix is implemented, also check other comments on the same PR, and ask user if the user wants to fix the rest of the comments as well. if said yes, then fetch all the comments validate the correctness and implement a minimal fix

👍 | 👎

[bito-code-review]

Code Review Agent Run #8680f9

Actionable Suggestions - 7

• superset/mcp_service/chart/schemas.py - 1

  • CWE-390: Bare Exception Handler · Line 491-496
• superset/mcp_service/chart/plugin.py - 1

  • Invalid type annotation string · Line 175-175
• superset/mcp_service/chart/tool/update_chart.py - 2

  • Redundant DB query in validation · Line 203-204
  • Missing test coverage for normalization · Line 416-432
• superset/mcp_service/chart/plugins/handlebars.py - 1

  • Duplicate validation with schema · Line 44-124
• tests/unit_tests/mcp_service/chart/validation/test_runtime_validator.py - 1

  • Test coverage regression · Line 213-234
• superset/mcp_service/chart/validation/runtime/__init__.py - 1

  • Avoid catching blind Exception · Line 107-107

Additional Suggestions - 6

• superset/initialization/__init__.py - 1

  • Missing error handling for MCP init · Line 750-750
    Wrap the call to `configure_mcp_chart_registry()` in a try-except block to catch any exceptions from `registry.configure()` and log them so that `init_app()` can continue even if MCP chart registry configuration fails.
• superset/mcp_service/chart/validation/schema_validator.py - 2

  • Import inside function body · Line 151-152
    The `from superset.mcp_service.chart.registry import get_registry` import is placed inside the function body at lines 151-152. This causes the import to execute on every call to `_pre_validate_chart_type`. Move it to the module-level imports for better performance.
    Code suggestion

    --- superset/mcp_service/chart/validation/schema_validator.py
    +++ superset/mcp_service/chart/validation/schema_validator.py
     @@ -29,6 +29,7 @@ from superset.mcp_service.chart.schemas import (
      )
      from superset.mcp_service.common.error_schemas import ChartGenerationError
    +from superset.mcp_service.chart.registry import get_registry
     
      logger = logging.getLogger(__name__)
     
     @@ -148,9 +149,6 @@ class SchemaValidator:
              config: Dict[str, Any],
          ) -> Tuple[bool, ChartGenerationError | None]:
              """Validate chart type and dispatch to plugin pre-validation."""
    -        from superset.mcp_service.chart.registry import get_registry
    -
              registry = get_registry()

  • Misleading variable name · Line 176-176
    Variable `valid_types` on line 176 is misleading — `registry.all_types()` returns only enabled chart types (per registry.py:168), so the name suggests all registered types when it actually contains only enabled ones. Rename to `enabled_types` for clarity.
    Code suggestion

    --- superset/mcp_service/chart/validation/schema_validator.py (lines 175-182)
    +++ superset/mcp_service/chart/validation/schema_validator.py
     @@ -173,12 +173,12 @@ class SchemaValidator:
                  )
     
              if not registry.is_enabled(chart_type):
    -            valid_types = ", ".join(registry.all_types())
    +            enabled_types = ", ".join(registry.all_types())
                  return False, ChartGenerationError(
                      error_type="disabled_chart_type",
                      message=f"Chart type '{chart_type}' is not enabled on this instance",
                      details=f"Chart type '{chart_type}' is registered but has been "
                      f"disabled by the operator. "
    -                f"Enabled chart types: {valid_types}",
    +                f"Enabled chart types: {enabled_types}",
                      suggestions=[
    -                    f"Use one of the enabled chart types: {valid_types}",
    +                    f"Use one of the enabled chart types: {enabled_types}",
                          "Contact your administrator if you believe this is an error",
                      ],
                      error_code="DISABLED_CHART_TYPE",
                  )

• superset/mcp_service/chart/plugins/xy.py - 3

  • Private member access in DatasetValidator · Line 114-114
    Accessing private method `_get_canonical_column_name`. Consider using a public API or adding a public wrapper method.
  • Private member access in DatasetValidator · Line 115-115
    Accessing private method `_get_canonical_metric_name`. Consider using a public API or adding a public wrapper method.
  • Private member access in DatasetValidator · Line 129-129
    Accessing private method `_normalize_filters`. Consider using a public API or adding a public wrapper method.

Filtered by Review Rules

Bito filtered these suggestions based on rules created automatically for your feedback. Manage rules.

• superset/mcp_service/chart/registry.py - 1

  • Broad exception catch · Line 87-87

Review Details

• Files reviewed - 27 · Commit Range: d3af275..f90f1cd

  • superset/initialization/__init__.py
  • superset/mcp_service/app.py
  • superset/mcp_service/chart/chart_utils.py
  • superset/mcp_service/chart/plugin.py
  • superset/mcp_service/chart/plugins/__init__.py
  • superset/mcp_service/chart/plugins/big_number.py
  • superset/mcp_service/chart/plugins/handlebars.py
  • superset/mcp_service/chart/plugins/mixed_timeseries.py
  • superset/mcp_service/chart/plugins/pie.py
  • superset/mcp_service/chart/plugins/pivot_table.py
  • superset/mcp_service/chart/plugins/table.py
  • superset/mcp_service/chart/plugins/xy.py
  • superset/mcp_service/chart/registry.py
  • superset/mcp_service/chart/schemas.py
  • superset/mcp_service/chart/tool/generate_chart.py
  • superset/mcp_service/chart/tool/update_chart.py
  • superset/mcp_service/chart/validation/dataset_validator.py
  • superset/mcp_service/chart/validation/runtime/__init__.py
  • superset/mcp_service/chart/validation/schema_validator.py
  • superset/mcp_service/flask_singleton.py
  • superset/mcp_service/mcp_config.py
  • tests/unit_tests/mcp_service/chart/test_big_number_chart.py
  • tests/unit_tests/mcp_service/chart/test_registry.py
  • tests/unit_tests/mcp_service/chart/test_registry_filters.py
  • tests/unit_tests/mcp_service/chart/tool/test_update_chart.py
  • tests/unit_tests/mcp_service/chart/validation/test_column_name_normalization.py
  • tests/unit_tests/mcp_service/chart/validation/test_runtime_validator.py
• Files skipped - 0
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[bito-code-review]

CWE-390: Bare Exception Handler

Replace the broad except Exception: with except (ImportError, AttributeError): to avoid masking unexpected errors while still handling expected import or attribute issues. (See also: CWE-390)

Code Review Run #8680f9

---

Should Bito avoid suggestions like this for future reviews? (Manage Rules)

• Yes, avoid them

[bito-code-review]

Invalid type annotation string

Remove the quotes around the return type annotation in the ChartTypePlugin protocol for schema_error_hint so it reads: def schema_error_hint(self) -> ChartGenerationError | None:.

Code Review Run #8680f9

---

Should Bito avoid suggestions like this for future reviews? (Manage Rules)

• Yes, avoid them

[bito-code-review]

Redundant DB query in validation

Lines 203-204 call DatasetValidator.validate_against_dataset(parsed_config, dataset.id) which internally invokes _get_dataset_context(dataset_id) to fetch the dataset from the database. However, _validate_update_against_dataset already has the dataset available from lines 178-180. This causes a redundant database query per update operation. Pass the pre-fetched dataset via the dataset_context parameter to eliminate the duplicate lookup.

Code suggestion

Check the AI-generated fix before applying

 --- superset/mcp_service/chart/tool/update_chart.py
 +++ superset/mcp_service/chart/tool/update_chart.py
 @@ -200,8 +200,14 @@ def _validate_update_against_dataset(
      # Column existence + fuzzy-match validation
      # (mirrors generate_chart pipeline layer 2)
      from superset.mcp_service.chart.validation.dataset_validator import DatasetValidator
 +    from superset.mcp_service.chart.compile import build_dataset_context_from_orm
 +
 +    # Reuse the already-fetched dataset to avoid a redundant DB query
 +    dataset_context = build_dataset_context_from_orm(dataset)
 
 -    is_col_valid, col_error = DatasetValidator.validate_against_dataset(
 -        parsed_config, dataset.id
 +    is_col_valid, col_error = DatasetValidator.validate_against_dataset(
 +        parsed_config, dataset.id, dataset_context=dataset_context
      )

Code Review Run #8680f9

---

Should Bito avoid suggestions like this for future reviews? (Manage Rules)

• Yes, avoid them

[bito-code-review]

Missing test coverage for normalization

The new column normalization feature (lines 416-432) has no test coverage. Tests should verify that column names are properly normalized to match dataset canonical names and that exceptions are handled gracefully without crashing the update operation.

Code Review Run #8680f9

---

Should Bito avoid suggestions like this for future reviews? (Manage Rules)

• Yes, avoid them

[bito-code-review]

Duplicate validation with schema

The pre_validate method duplicates validation logic already implemented in HandlebarsChartConfig.validate_query_fields() (schemas.py:1055-1087). This includes checking for missing metrics in aggregate mode (line 109) and missing columns in raw mode (line 94). The schema validator also performs additional checks (mutual exclusivity, aggregate function presence) that pre_validate lacks. Having both creates maintenance divergence risk.

Code Review Run #8680f9

---

Should Bito avoid suggestions like this for future reviews? (Manage Rules)

• Yes, avoid them

[bito-code-review]

Test coverage regression

Test test_validate_table_chart_skips_xy_validations no longer verifies that XY-specific validations are skipped. Original assertions mock_format.assert_not_called() and mock_cardinality.assert_not_called() were removed, weakening test coverage for the skip behavior.

Code Review Run #8680f9

---

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• Yes, avoid them

[bito-code-review]

Avoid catching blind Exception

Replace bare Exception catch with specific exception types (e.g., ImportError, AttributeError, RuntimeError) to avoid masking unexpected errors.

Code suggestion

Check the AI-generated fix before applying

Suggested change

	except Exception as exc:
	except (ImportError, AttributeError, RuntimeError) as exc:

Code Review Run #8680f9

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• Yes, avoid them

[bito-code-review]

Code Review Agent Run #40e077

Actionable Suggestions - 0

Review Details

• Files reviewed - 2 · Commit Range: f90f1cd..42a293a

  • superset/mcp_service/chart/plugin.py
  • superset/mcp_service/chart/schemas.py
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