codeant-ai-for-open-source[bot] commented on code in PR #40960:
URL: https://github.com/apache/superset/pull/40960#discussion_r3482433933
##########
superset/mcp_service/dashboard/schemas.py:
##########
@@ -1512,3 +1512,207 @@ def dashboard_layout_serializer(dashboard: "Dashboard")
-> DashboardLayout:
has_layout=bool(position_json_str),
)
)
+
+
+# ---------------------------------------------------------------------------
+# manage_native_filters schemas
+# ---------------------------------------------------------------------------
+
+
+class BaseNewFilterSpec(BaseModel):
+ """Common fields shared by all new native filter specs."""
+
+ name: str = Field(..., min_length=1, description="Filter display name")
+ description: str = Field("", description="Optional filter description")
+ scope_chart_ids: List[int] | None = Field(
+ None,
+ description=(
+ "Chart IDs this filter should apply to. When omitted the filter "
+ "applies to all charts on the dashboard. All IDs must belong to "
+ "charts that are on the dashboard."
+ ),
+ )
+
+
+class FilterSelectSpec(BaseNewFilterSpec):
+ """Spec for a new dropdown (filter_select) native filter."""
+
+ filter_type: Literal["filter_select"] = Field(
+ ..., description="Discriminator - must be 'filter_select'"
+ )
+ dataset_id: int = Field(..., description="ID of the dataset to filter on")
+ column: str = Field(
+ ..., min_length=1, description="Name of the dataset column to filter
on"
+ )
+ multi_select: bool = Field(
+ True, description="Allow selecting multiple values (default True)"
+ )
+ default_to_first_item: bool = Field(
+ False, description="Default the filter to the first item in the list"
+ )
+ enable_empty_filter: bool = Field(
+ False, description="Require a value before the filter is applied"
+ )
+ sort_ascending: bool | None = Field(
+ None,
+ description=(
+ "Sort filter values ascending (True) or descending (False). "
+ "When omitted, values are not explicitly sorted."
+ ),
+ )
+ search_all_options: bool = Field(
+ False, description="Query the database on search rather than
client-side"
+ )
+
+
+class FilterTimeSpec(BaseNewFilterSpec):
+ """Spec for a new time range (filter_time) native filter."""
+
+ filter_type: Literal["filter_time"] = Field(
+ ..., description="Discriminator - must be 'filter_time'"
+ )
+ default_time_range: str | None = Field(
+ None,
+ description=(
+ "Default time range value, e.g. 'Last week', 'Last month', "
+ "'2024-01-01 : 2024-12-31'. When omitted the filter has no
default."
+ ),
+ )
+
+
+NewNativeFilterSpec = Annotated[
+ FilterSelectSpec | FilterTimeSpec,
+ Field(discriminator="filter_type"),
+]
+
+
+class NativeFilterUpdateSpec(BaseModel):
+ """Partial update for an existing native filter.
+
+ Only ``id`` is required; any other provided field is merged into the
+ existing filter configuration. Fields that only apply to one filter
+ type (e.g. ``multi_select`` for filter_select, ``default_time_range``
+ for filter_time) are rejected when used on the wrong filter type.
+ """
+
+ id: str = Field(..., min_length=1, description="ID of the filter to
update")
+ name: str | None = Field(None, min_length=1, description="New display
name")
+ description: str | None = Field(None, description="New description")
+ dataset_id: int | None = Field(
+ None, description="New target dataset ID (filter_select only)"
+ )
+ column: str | None = Field(
+ None, min_length=1, description="New target column name (filter_select
only)"
+ )
+ multi_select: bool | None = Field(
+ None, description="Allow multiple values (filter_select only)"
+ )
+ default_to_first_item: bool | None = Field(
+ None, description="Default to first item (filter_select only)"
+ )
+ enable_empty_filter: bool | None = Field(
+ None, description="Require a value (filter_select only)"
+ )
+ sort_ascending: bool | None = Field(
+ None, description="Sort values ascending/descending (filter_select
only)"
+ )
+ search_all_options: bool | None = Field(
+ None, description="Search all options in the database (filter_select
only)"
+ )
+ default_time_range: str | None = Field(
+ None, description="Default time range (filter_time only)"
+ )
+ scope_chart_ids: List[int] | None = Field(
+ None,
+ description=(
+ "Chart IDs this filter should apply to. Replaces the current "
+ "scope. All IDs must belong to charts on the dashboard."
+ ),
+ )
+
+
+class ManageNativeFiltersRequest(BaseModel):
+ """Request schema for the manage_native_filters tool."""
+
+ dashboard_id: int = Field(..., description="ID of the dashboard to modify")
+ add: List[NewNativeFilterSpec] = Field(
+ default_factory=list,
+ description=(
+ "New filters to create. Supported types: filter_select "
+ "(dropdown) and filter_time (time range). Other filter types "
+ "(numerical range, time column, time grain) are not yet "
+ "supported by this tool."
+ ),
+ )
+ update: List[NativeFilterUpdateSpec] = Field(
+ default_factory=list,
+ description="Partial updates to existing filters, addressed by filter
ID",
+ )
+ remove: List[str] = Field(
+ default_factory=list,
+ description="IDs of filters to delete from the dashboard",
+ )
+ reorder: List[str] | None = Field(
+ None,
+ description=(
+ "Complete ordered list of filter IDs defining the new filter "
+ "order. Must include every filter that remains on the dashboard "
+ "(after removals); newly added filters are appended "
+ "automatically and may be omitted."
+ ),
+ )
+
+ @model_validator(mode="after")
+ def _require_at_least_one_operation(self) -> "ManageNativeFiltersRequest":
+ """Reject requests that specify no add/update/remove/reorder
operation."""
+ if not self.add and not self.update and not self.remove and not
self.reorder:
Review Comment:
**Suggestion:** The validator treats an explicit empty reorder list as "no
operation" because it uses a falsy check (`not self.reorder`), so requests like
`{"reorder": []}` are rejected even when they are semantically valid (for
example, dashboards with no remaining filters). Check `self.reorder is None`
instead so an explicitly provided empty list counts as a reorder operation.
[incorrect condition logic]
<details>
<summary><b>Severity Level:</b> Major ⚠️</summary>
```mdx
- ⚠️ Explicit reorder-only requests with empty list rejected.
- ⚠️ Dashboards without filters cannot express reorder-only intent.
```
</details>
<details>
<summary><b>Steps of Reproduction ✅ </b></summary>
```mdx
1. Call the MCP tool `manage_native_filters` (entrypoint at
`superset/mcp_service/dashboard/tool/manage_native_filters.py:387`) via the
MCP server
with a JSON body that includes only `dashboard_id` and an explicit empty
reorder list,
e.g. `{"dashboard_id": 123, "reorder": []}`.
2. FastMCP constructs a `ManageNativeFiltersRequest` Pydantic model (defined
at
`superset/mcp_service/dashboard/schemas.py:145-183`), which triggers the
`@model_validator` `_require_at_least_one_operation` at
`superset/mcp_service/dashboard/schemas.py:176-183`.
3. In `_require_at_least_one_operation`, the condition `if not self.add and
not
self.update and not self.remove and not self.reorder:` at line `1668`
evaluates to True
because `add`, `update`, `remove`, and the explicitly provided `reorder`
field are all
empty lists (falsy).
4. The validator raises `ValueError("At least one operation (add, update,
remove, reorder)
is required")`, so the request is rejected before reaching
`manage_native_filters()` and
`_build_native_filters_payload()` (defined at
`superset/mcp_service/dashboard/tool/manage_native_filters.py:294-375`),
even though an
explicit empty `reorder` is a semantically valid no-op for dashboards with
no remaining
filters.
```
</details>
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<details>
<summary><b>Prompt for AI Agent 🤖 </b></summary>
```mdx
This is a comment left during a code review.
**Path:** superset/mcp_service/dashboard/schemas.py
**Line:** 1668:1668
**Comment:**
*Incorrect Condition Logic: The validator treats an explicit empty
reorder list as "no operation" because it uses a falsy check (`not
self.reorder`), so requests like `{"reorder": []}` are rejected even when they
are semantically valid (for example, dashboards with no remaining filters).
Check `self.reorder is None` instead so an explicitly provided empty list
counts as a reorder operation.
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
```
</details>
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href='https://app.codeant.ai/feedback?pr_url=https%3A%2F%2Fgithub.com%2Fapache%2Fsuperset%2Fpull%2F40960&comment_hash=2944ad3d8ef2f2859b832a5d5869c1b22a78ff8a5ea5fb1be0eb592b5c2a9b7a&reaction=like'>👍</a>
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##########
superset/mcp_service/dashboard/tool/manage_native_filters.py:
##########
@@ -0,0 +1,497 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+MCP tool: manage_native_filters
+
+Adds, updates, removes, and reorders native filters on a dashboard by
+translating high-level operations into the ``deleted`` / ``modified`` /
+``reordered`` payload consumed by ``UpdateDashboardNativeFiltersCommand``.
+"""
+
+import copy
+import logging
+from typing import Any, cast
+
+from fastmcp import Context
+from superset_core.mcp.decorators import tool, ToolAnnotations
+
+from superset.extensions import event_logger
+from superset.mcp_service.dashboard.constants import generate_id
+from superset.mcp_service.dashboard.schemas import (
+ FilterSelectSpec,
+ FilterTimeSpec,
+ ManageNativeFiltersRequest,
+ ManageNativeFiltersResponse,
+ NativeFilterSummary,
+ NativeFilterUpdateSpec,
+)
+from superset.mcp_service.utils import sanitize_for_llm_context
+from superset.mcp_service.utils.url_utils import get_superset_base_url
+from superset.utils import json
+
+logger = logging.getLogger(__name__)
+
+# Control values that map to filter_select controlValues keys.
+_SELECT_CONTROL_FIELDS: dict[str, str] = {
+ "multi_select": "multiSelect",
+ "default_to_first_item": "defaultToFirstItem",
+ "enable_empty_filter": "enableEmptyFilter",
+ "sort_ascending": "sortAscending",
+ "search_all_options": "searchAllOptions",
+}
+
+
+class _FilterValidationError(Exception):
+ """Raised internally when a filter operation fails validation."""
+
+
+def _empty_data_mask() -> dict[str, Any]:
+ """Return the default data mask for a filter with no applied value."""
+ return {"filterState": {"value": None}, "extraFormData": {}}
+
+
+def _time_data_mask(default_time_range: str | None) -> dict[str, Any]:
+ """Build the default data mask for a time filter.
+
+ When ``default_time_range`` is empty the filter starts unset (the empty
+ mask); otherwise the range is applied as both the filter state value and
+ the ``time_range`` extra form data.
+ """
+ if not default_time_range:
+ return _empty_data_mask()
+ return {
+ "filterState": {"value": default_time_range},
+ "extraFormData": {"time_range": default_time_range},
+ }
+
+
+def _validate_dataset_column(dataset_id: int, column: str) -> None:
+ """Validate that the dataset exists and contains the given column."""
+ from superset.daos.dataset import DatasetDAO
+
+ dataset = DatasetDAO.find_by_id(dataset_id)
+ if not dataset:
+ raise _FilterValidationError(
+ f"Dataset with ID {dataset_id} not found."
+ " Use list_datasets to get valid dataset IDs."
+ )
+ column_names = [c.column_name for c in dataset.columns]
+ if column not in column_names:
+ raise _FilterValidationError(
+ f"Column '{column}' not found in dataset {dataset_id}. "
+ f"Available columns: {', '.join(sorted(column_names))}."
+ )
+
+
+def _build_scope(
+ scope_chart_ids: list[int] | None,
+ dashboard_chart_ids: list[int],
+) -> dict[str, Any]:
+ """Translate scope_chart_ids into the frontend scope structure.
+
+ The frontend expresses scope as an exclusion list, so charts NOT in
+ ``scope_chart_ids`` are excluded. When ``scope_chart_ids`` is None
+ the filter applies to all charts (empty exclusion list).
+ """
+ if scope_chart_ids is None:
+ return {"rootPath": ["ROOT_ID"], "excluded": []}
+ unknown = sorted(set(scope_chart_ids) - set(dashboard_chart_ids))
+ if unknown:
+ raise _FilterValidationError(
+ f"scope_chart_ids contains chart IDs not on the dashboard: "
+ f"{unknown}. Charts on this dashboard:
{sorted(dashboard_chart_ids)}."
+ )
+ excluded = sorted(set(dashboard_chart_ids) - set(scope_chart_ids))
+ return {"rootPath": ["ROOT_ID"], "excluded": excluded}
+
+
+def _build_new_filter_config(
+ spec: FilterSelectSpec | FilterTimeSpec,
+ dashboard_chart_ids: list[int],
+) -> dict[str, Any]:
+ """Build a full native filter config dict for a new filter."""
+ scope = _build_scope(spec.scope_chart_ids, dashboard_chart_ids)
+ filter_id = generate_id("NATIVE_FILTER")
+
+ if isinstance(spec, FilterSelectSpec):
+ _validate_dataset_column(spec.dataset_id, spec.column)
+ control_values: dict[str, Any] = {
+ "multiSelect": spec.multi_select,
+ "defaultToFirstItem": spec.default_to_first_item,
+ "enableEmptyFilter": spec.enable_empty_filter,
+ "searchAllOptions": spec.search_all_options,
+ }
+ if spec.sort_ascending is not None:
+ control_values["sortAscending"] = spec.sort_ascending
+ return {
+ "id": filter_id,
+ "type": "NATIVE_FILTER",
+ "filterType": "filter_select",
+ "name": spec.name,
+ "description": spec.description,
+ "scope": scope,
+ "targets": [
+ {"datasetId": spec.dataset_id, "column": {"name": spec.column}}
+ ],
+ "controlValues": control_values,
+ "defaultDataMask": _empty_data_mask(),
+ "cascadeParentIds": [],
+ }
+
+ # filter_time: no dataset target, empty controlValues
+ return {
+ "id": filter_id,
+ "type": "NATIVE_FILTER",
+ "filterType": "filter_time",
+ "name": spec.name,
+ "description": spec.description,
+ "scope": scope,
+ "targets": [{}],
+ "controlValues": {},
+ "defaultDataMask": _time_data_mask(spec.default_time_range),
+ "cascadeParentIds": [],
+ }
+
+
+def _validate_update_type_compat(
+ spec: NativeFilterUpdateSpec, filter_type: str | None
+) -> None:
+ """Reject update fields that do not apply to the filter's type."""
+ select_fields_set = [
+ field
+ for field in (*_SELECT_CONTROL_FIELDS, "dataset_id", "column")
+ if getattr(spec, field) is not None
+ ]
+ if filter_type != "filter_select" and select_fields_set:
+ raise _FilterValidationError(
+ f"Filter '{spec.id}' has type '{filter_type}'; fields "
+ f"{select_fields_set} only apply to filter_select filters."
+ )
+ if filter_type != "filter_time" and spec.default_time_range is not None:
+ raise _FilterValidationError(
+ f"Filter '{spec.id}' has type '{filter_type}'; default_time_range "
+ "only applies to filter_time filters."
+ )
+
+
+def _merge_target(spec: NativeFilterUpdateSpec, merged: dict[str, Any]) ->
None:
+ """Merge dataset_id / column changes into the filter's first target."""
+ targets = merged.get("targets") or [{}]
+ target = dict(targets[0]) if targets else {}
+ dataset_id = (
+ spec.dataset_id if spec.dataset_id is not None else
target.get("datasetId")
+ )
+ column = (
+ spec.column
+ if spec.column is not None
+ else (target.get("column") or {}).get("name")
+ )
+ if dataset_id is None or not column:
+ raise _FilterValidationError(
+ f"Filter '{spec.id}' is missing a dataset or column target; "
+ "provide both dataset_id and column to set the target."
+ )
+ _validate_dataset_column(dataset_id, column)
+ target["datasetId"] = dataset_id
+ target["column"] = {"name": column}
+ merged["targets"] = [target]
+
+
+def _merge_filter_update(
+ spec: NativeFilterUpdateSpec,
+ existing: dict[str, Any],
+ dashboard_chart_ids: list[int],
+) -> dict[str, Any]:
+ """Merge a partial update into an existing filter config.
+
+ Returns a FULL filter config (the backend command substitutes whole
+ entries, it does not merge deltas).
+ """
+ merged = copy.deepcopy(existing)
+ _validate_update_type_compat(spec, merged.get("filterType"))
+
+ if spec.name is not None:
+ merged["name"] = spec.name
+ if spec.description is not None:
+ merged["description"] = spec.description
+ if spec.scope_chart_ids is not None:
+ merged["scope"] = _build_scope(spec.scope_chart_ids,
dashboard_chart_ids)
+ if spec.dataset_id is not None or spec.column is not None:
+ _merge_target(spec, merged)
+
+ control_values = dict(merged.get("controlValues") or {})
+ for field, control_key in _SELECT_CONTROL_FIELDS.items():
+ value = getattr(spec, field)
+ if value is not None:
+ control_values[control_key] = value
+ merged["controlValues"] = control_values
+
+ if spec.default_time_range is not None:
+ merged["defaultDataMask"] = _time_data_mask(spec.default_time_range)
+
+ return merged
+
+
+def _filter_summary(conf: dict[str, Any]) -> NativeFilterSummary:
+ """Summarize a filter config for the response.
+
+ Returns the id, name, filterType, and non-empty targets; empty target
+ entries (e.g. for time filters) are dropped so the summary only lists
+ real dataset/column targets. The user-controlled ``name`` and ``targets``
+ come from dashboard metadata and are wrapped as untrusted content before
+ being exposed to LLM context (mirroring the get_dashboard_info read path).
+ """
+ name = conf.get("name")
+ targets = [t for t in (conf.get("targets") or []) if t]
+ return NativeFilterSummary(
+ id=conf.get("id"),
+ name=sanitize_for_llm_context(name, field_path=("name",))
+ if name is not None
+ else None,
+ filter_type=conf.get("filterType"),
+ targets=cast(
+ list[dict[str, Any]],
+ sanitize_for_llm_context(
+ targets,
+ field_path=("targets",),
+ excluded_field_names=frozenset(),
+ ),
+ ),
+ )
+
+
+def _current_native_filter_config(dashboard: Any) -> list[dict[str, Any]]:
+ """Return the dashboard's existing native filter configuration.
+
+ ``json_metadata`` may be missing, invalid JSON, or parse to a non-dict
+ (e.g. a legacy ``"[]"`` payload); all of those degrade to an empty list
+ rather than raising.
+ """
+ try:
+ metadata = json.loads(dashboard.json_metadata or "{}")
+ except (json.JSONDecodeError, TypeError):
+ metadata = {}
+ if not isinstance(metadata, dict):
+ return []
+ return metadata.get("native_filter_configuration") or []
+
+
+def _build_native_filters_payload( # noqa: C901
+ request: ManageNativeFiltersRequest,
+ current_config: list[dict[str, Any]],
+ dashboard_chart_ids: list[int],
+) -> tuple[dict[str, Any], list[str], list[str]]:
+ """Translate tool operations into the command payload.
+
+ Returns ``(payload, added_filter_ids, updated_filter_ids)`` where the
+ payload has the ``deleted`` / ``modified`` / ``reordered`` shape expected
+ by ``UpdateDashboardNativeFiltersCommand``.
+ """
+ current_by_id = {conf["id"]: conf for conf in current_config if
conf.get("id")}
Review Comment:
**Suggestion:** `native_filter_configuration` is returned without type
validation, so malformed dashboard metadata (for example a string, dict, or
list containing non-dict items) can flow into payload building and crash on
`conf.get(...)`/`conf["id"]` with an `AttributeError`/`TypeError`. Validate
that this field is a list of dicts (and drop invalid entries) before returning
it so the tool degrades gracefully instead of failing at runtime. [type error]
<details>
<summary><b>Severity Level:</b> Major ⚠️</summary>
```mdx
- ❌ manage_native_filters crashes on malformed native_filter_configuration.
- ⚠️ Dashboards with bad metadata cannot manage native filters.
```
</details>
<details>
<summary><b>Steps of Reproduction ✅ </b></summary>
```mdx
1. Ensure there is a dashboard record with `json_metadata` containing a
malformed
`native_filter_configuration`, for example
`{\"native_filter_configuration\": [\"oops\"]}`
or `{\"native_filter_configuration\": \"not-a-list\"}`, so that
`metadata.get(\"native_filter_configuration\")` does not return a list of
dicts.
2. Call the MCP tool `manage_native_filters` (defined at
`superset/mcp_service/dashboard/tool/manage_native_filters.py:387-138`) with
a valid
`dashboard_id` for that dashboard and any non-empty operation (e.g. `update`
or `remove`)
so the tool executes payload building.
3. Inside `manage_native_filters`, `DashboardDAO.find_by_id()` loads the
dashboard, and
`_current_native_filter_config(dashboard)` is called at
`superset/mcp_service/dashboard/tool/manage_native_filters.py:421`; this
function returns
`metadata.get("native_filter_configuration") or []` at line `291`, which in
this scenario
is a malformed value (e.g. list of strings).
4. `_build_native_filters_payload(request, current_config,
dashboard_chart_ids)` is then
invoked at
`superset/mcp_service/dashboard/tool/manage_native_filters.py:425`; its
`current_by_id = {conf["id"]: conf for conf in current_config if
conf.get("id")}` at line
`305` iterates over non-dict entries (like strings), causing an
`AttributeError` or
`TypeError` when accessing `conf["id"]`/`conf.get(...)`, which is not caught
by the
specific `Dashboard*Error` handlers and instead triggers the generic `except
Exception`
block at `manage_native_filters.py:132-138`, resulting in a logged exception
and tool
failure.
```
</details>
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*(Use Cmd/Ctrl + Click for best experience)*
<details>
<summary><b>Prompt for AI Agent 🤖 </b></summary>
```mdx
This is a comment left during a code review.
**Path:** superset/mcp_service/dashboard/tool/manage_native_filters.py
**Line:** 291:305
**Comment:**
*Type Error: `native_filter_configuration` is returned without type
validation, so malformed dashboard metadata (for example a string, dict, or
list containing non-dict items) can flow into payload building and crash on
`conf.get(...)`/`conf["id"]` with an `AttributeError`/`TypeError`. Validate
that this field is a list of dicts (and drop invalid entries) before returning
it so the tool degrades gracefully instead of failing at runtime.
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
```
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