codeant-ai-for-open-source[bot] commented on code in PR #40960:
URL: https://github.com/apache/superset/pull/40960#discussion_r3493345907
##########
superset/daos/dashboard.py:
##########
@@ -431,7 +431,8 @@ def update_native_filters_config(
raise DashboardUpdateFailedError("Dashboard not found")
if attributes:
- metadata = json.loads(dashboard.json_metadata or "{}")
+ _parsed = json.loads(dashboard.json_metadata or "{}")
+ metadata = _parsed if isinstance(_parsed, dict) else {}
Review Comment:
**Suggestion:** This parsing path still throws on malformed `json_metadata`,
so dashboards with invalid JSON will fail native-filter updates at write time
even though the MCP tool now handles malformed metadata defensively during
read/validation. Wrap this parse in exception handling and fall back to `{}` to
avoid runtime write failures on legacy/corrupt rows. [possible bug]
<details>
<summary><b>Severity Level:</b> Major ⚠️</summary>
```mdx
- ❌ MCP manage_native_filters tool crashes on corrupt metadata.
- ⚠️ Native filter updates fail on dashboards with bad json_metadata.
```
</details>
<details>
<summary><b>Steps of Reproduction ✅ </b></summary>
```mdx
1. Ensure there is a dashboard row whose `json_metadata` column contains
invalid JSON (for
example, via a manual DB update) for an existing dashboard id; this field is
later
consumed without validation by `DashboardDAO.update_native_filters_config` in
`superset/daos/dashboard.py:425-439` (verified via Read on that file).
2. Call the MCP tool `manage_native_filters` with `dashboard_id` set to that
dashboard, so
that the tool reaches the write path; `manage_native_filters` in
`superset/mcp_service/dashboard/tool/manage_native_filters.py:19-88` builds
a payload and
then invokes `UpdateDashboardNativeFiltersCommand(request.dashboard_id,
payload).run()` at
lines 66-69 (Read output).
3. In `UpdateDashboardNativeFiltersCommand.run` (see
`superset/commands/dashboard/update.py:255-259` from Grep), the command calls
`DashboardDAO.update_native_filters_config(self._model, self._properties)`,
sending the
MCP payload and the loaded dashboard model into the DAO layer.
4. Inside `DashboardDAO.update_native_filters_config` at
`superset/daos/dashboard.py:434-439`, the code executes `_parsed =
json.loads(dashboard.json_metadata or "{}")` without a try/except; because
`dashboard.json_metadata` is malformed JSON, `json.loads` raises
`json.JSONDecodeError`,
which is not handled by the DAO or by `UpdateDashboardNativeFiltersCommand`,
and the outer
`manage_native_filters` handler only logs and re-raises unexpected
exceptions at
`superset/mcp_service/dashboard/tool/manage_native_filters.py:123-129`,
resulting in an
unhandled error for the caller when attempting to update native filters on
that dashboard.
```
</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/daos/dashboard.py
**Line:** 434:435
**Comment:**
*Possible Bug: This parsing path still throws on malformed
`json_metadata`, so dashboards with invalid JSON will fail native-filter
updates at write time even though the MCP tool now handles malformed metadata
defensively during read/validation. Wrap this parse in exception handling and
fall back to `{}` to avoid runtime write failures on legacy/corrupt rows.
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>
<a
href='https://app.codeant.ai/feedback?pr_url=https%3A%2F%2Fgithub.com%2Fapache%2Fsuperset%2Fpull%2F40960&comment_hash=03d5b006b51ffc67da2d4662c16c7b621b91f6b9777ef78e24ea086c11edf891&reaction=like'>👍</a>
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href='https://app.codeant.ai/feedback?pr_url=https%3A%2F%2Fgithub.com%2Fapache%2Fsuperset%2Fpull%2F40960&comment_hash=03d5b006b51ffc67da2d4662c16c7b621b91f6b9777ef78e24ea086c11edf891&reaction=dislike'>👎</a>
##########
superset/mcp_service/dashboard/tool/manage_native_filters.py:
##########
@@ -0,0 +1,508 @@
+# 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 (
+ escape_llm_context_delimiters,
+ 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))}."
+ )
Review Comment:
**Suggestion:** The dataset/column validator leaks schema details by
returning all column names for any dataset ID, and it does so without an
explicit dataset-access check. This enables metadata enumeration through error
messages; return a generic validation error (or enforce datasource permission
first) instead of exposing full column lists. [security]
<details>
<summary><b>Severity Level:</b> Major ⚠️</summary>
```mdx
- ⚠️ Dataset existence discoverable through manage_native_filters errors.
- ⚠️ Dataset column names exposed to potentially unauthorized MCP callers.
```
</details>
<details>
<summary><b>Steps of Reproduction ✅ </b></summary>
```mdx
1. Using the MCP server, a user who can invoke `manage_native_filters` but
does not
necessarily have access to all datasets prepares a request that adds or
updates a
`filter_select` filter targeting a dataset id they want to probe. The
request uses that
`dataset_id` and a bogus `column` name. New filters flow into
`_build_new_filter_config`
at `superset/mcp_service/dashboard/tool/manage_native_filters.py:47-78`,
which calls
`_validate_dataset_column(spec.dataset_id, spec.column)` for
`FilterSelectSpec` instances.
2. `_validate_dataset_column` is implemented at
`superset/mcp_service/dashboard/tool/manage_native_filters.py:86-101` and is
invoked both
from `_build_new_filter_config` (line 56) and from `_merge_target` during
updates (line
212). This helper calls `DatasetDAO.find_by_id(dataset_id)` without any
dataset-level
permission enforcement and, if a dataset object is returned, immediately
iterates its
`columns` relationship.
3. When the dataset exists but the requested column name is invalid,
`_validate_dataset_column` computes `column_names = [c.column_name for c in
dataset.columns]` and checks `if column not in column_names:`; it then raises
`_FilterValidationError` with an error string that includes `"Column
'{column}' not found
in dataset {dataset_id}. Available columns: {',
'.join(sorted(column_names))}."`, thereby
embedding the full list of column names for that dataset into the exception
message.
4. Back in `manage_native_filters`, this `_FilterValidationError` is caught
at
`superset/mcp_service/dashboard/tool/manage_native_filters.py:56-64`, and
the function
returns `ManageNativeFiltersResponse(dashboard_id=request.dashboard_id,
error=str(exc))`
to the caller. As a result, a caller who can reach the MCP tool but lacks any
dataset-level authorization can iteratively choose dataset ids and invalid
column names,
observe whether the error changes from "Dataset with ID X not found" to
"Available
columns: ...", and thereby confirm dataset existence and enumerate all
column names for
datasets that should otherwise be opaque.
```
</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:** 90:101
**Comment:**
*Security: The dataset/column validator leaks schema details by
returning all column names for any dataset ID, and it does so without an
explicit dataset-access check. This enables metadata enumeration through error
messages; return a generic validation error (or enforce datasource permission
first) instead of exposing full column lists.
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>
<a
href='https://app.codeant.ai/feedback?pr_url=https%3A%2F%2Fgithub.com%2Fapache%2Fsuperset%2Fpull%2F40960&comment_hash=915ab8e56f2eb45ec754b7c7dc85cec2733ca216ae6ec8cad6b8c91ddd5d27a5&reaction=like'>👍</a>
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##########
superset/mcp_service/dashboard/tool/manage_native_filters.py:
##########
@@ -0,0 +1,508 @@
+# 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 (
+ escape_llm_context_delimiters,
+ 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).
+ The operational ``id`` and ``filter_type`` fields are delimiter-escaped
+ (not wrapped) so the LLM can pass them back verbatim in subsequent calls
+ while any embedded delimiter tokens are neutralized.
+ """
+ name = conf.get("name")
+ targets = [t for t in (conf.get("targets") or []) if t]
+ return NativeFilterSummary(
+ id=escape_llm_context_delimiters(conf.get("id")),
+ name=sanitize_for_llm_context(name, field_path=("name",))
+ if name is not None
+ else None,
+ filter_type=escape_llm_context_delimiters(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 []
+ config = metadata.get("native_filter_configuration")
+ if not isinstance(config, list):
+ return []
+ # Drop malformed (non-dict) entries so downstream conf["id"] /
conf.get(...)
+ # cannot raise on corrupt metadata.
+ return [item for item in config if isinstance(item, dict)]
+
+
+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")}
+
+ unknown_removals = [fid for fid in request.remove if fid not in
current_by_id]
+ if unknown_removals:
+ raise _FilterValidationError(
+ f"Cannot remove filters that do not exist on the dashboard: "
+ f"{unknown_removals}. Existing filter IDs: "
+ f"{sorted(current_by_id)}."
+ )
+
+ removed_ids = set(request.remove)
+ modified: list[dict[str, Any]] = []
+ updated_filter_ids: list[str] = []
+
+ update_ids = [update_spec.id for update_spec in request.update]
+ duplicate_updates = sorted({fid for fid in update_ids if
update_ids.count(fid) > 1})
+ if duplicate_updates:
+ raise _FilterValidationError(
+ f"update contains duplicate filter IDs: {duplicate_updates}. "
+ "Provide at most one update per filter."
+ )
+
+ for update_spec in request.update:
+ if update_spec.id in removed_ids:
+ raise _FilterValidationError(
+ f"Filter '{update_spec.id}' cannot be both updated and
removed."
+ )
+ existing = current_by_id.get(update_spec.id)
+ if existing is None:
+ raise _FilterValidationError(
+ f"Cannot update filter '{update_spec.id}': not found on the "
+ f"dashboard. Existing filter IDs: {sorted(current_by_id)}."
+ )
+ modified.append(
+ _merge_filter_update(update_spec, existing, dashboard_chart_ids)
+ )
+ updated_filter_ids.append(update_spec.id)
+
+ added_filter_ids: list[str] = []
+ for new_spec in request.add:
+ config = _build_new_filter_config(new_spec, dashboard_chart_ids)
+ modified.append(config)
+ added_filter_ids.append(config["id"])
+
+ payload: dict[str, Any] = {}
+ if request.remove:
+ payload["deleted"] = list(request.remove)
+ if modified:
+ payload["modified"] = modified
+
+ if request.reorder is not None:
+ # The DAO drops any surviving filter that is absent from the
+ # reordered list, so require a complete ordering of surviving
+ # pre-existing filters. Newly added filters are appended
+ # automatically by the DAO and may be omitted.
+ surviving_ids = set(current_by_id) - removed_ids
+ reorder_ids = [fid for fid in request.reorder if fid not in
added_filter_ids]
+ if len(set(request.reorder)) != len(request.reorder):
+ raise _FilterValidationError("reorder contains duplicate filter
IDs.")
+ missing = sorted(surviving_ids - set(reorder_ids))
+ unknown = sorted(set(reorder_ids) - surviving_ids)
+ if missing or unknown:
+ raise _FilterValidationError(
+ "reorder must list every remaining filter exactly once. "
+ f"Missing: {missing}. Unknown: {unknown}. "
+ f"Remaining filter IDs: {sorted(surviving_ids)}."
+ )
+ payload["reordered"] = list(request.reorder)
+
+ return payload, added_filter_ids, updated_filter_ids
+
+
+@tool(
+ tags=["mutate"],
+ class_permission_name="Dashboard",
+ method_permission_name="write",
+ annotations=ToolAnnotations(
+ title="Manage dashboard native filters",
+ readOnlyHint=False,
+ destructiveHint=True,
+ ),
+)
+def manage_native_filters(
+ request: ManageNativeFiltersRequest, ctx: Context
+) -> ManageNativeFiltersResponse:
+ """
+ Add, update, remove, and reorder native filters on a dashboard.
+
+ Supported filter types for new filters: filter_select (dropdown backed
+ by a dataset column) and filter_time (time range). Other filter types
+ (numerical range, time column, time grain) are not yet supported.
+ Filter IDs are generated by the server and returned in the response.
+ """
+ from superset.commands.dashboard.exceptions import (
+ DashboardForbiddenError,
+ DashboardInvalidError,
+ DashboardNativeFiltersUpdateFailedError,
+ DashboardNotFoundError,
+ )
+ from superset.commands.dashboard.update import (
+ UpdateDashboardNativeFiltersCommand,
+ )
+ from superset.commands.exceptions import TagForbiddenError
+ from superset.daos.dashboard import DashboardDAO
+
+ try:
+ with
event_logger.log_context(action="mcp.manage_native_filters.validation"):
+ dashboard = DashboardDAO.find_by_id(request.dashboard_id)
+ if not dashboard:
+ return ManageNativeFiltersResponse(
+ error=(
+ f"Dashboard with ID {request.dashboard_id} not found."
+ " Use list_dashboards to get valid dashboard IDs."
+ ),
+ )
+
+ current_config = _current_native_filter_config(dashboard)
+ dashboard_chart_ids = [slc.id for slc in dashboard.slices]
+
+ try:
+ payload, added_ids, updated_ids =
_build_native_filters_payload(
+ request, current_config, dashboard_chart_ids
+ )
Review Comment:
**Suggestion:** The tool reads `dashboard.json_metadata` and
`dashboard.slices` and runs detailed validation before ownership is enforced by
the command layer. This lets non-owners trigger validation errors that reveal
internal dashboard details (existing filter IDs, chart IDs, etc.) instead of
getting a straight permission-denied response. Perform an ownership check
immediately after loading the dashboard and before building payload/validation
output. [security]
<details>
<summary><b>Severity Level:</b> Major ⚠️</summary>
```mdx
- ⚠️ Non-owners can enumerate native filter IDs via errors.
- ⚠️ Non-owners can enumerate dashboard chart IDs via errors.
```
</details>
<details>
<summary><b>Steps of Reproduction ✅ </b></summary>
```mdx
1. Start the MCP server so the `manage_native_filters` tool is available;
the tool is
defined in
`superset/mcp_service/dashboard/tool/manage_native_filters.py:19-88` and
registered via `superset/mcp_service/dashboard/tool/__init__.py` (per code
graph context).
Assume a user who can call MCP tools and has the global Dashboard write
permission but is
not an owner of a specific dashboard id D (ownership enforcement happens in
the dashboard
command layer).
2. That user calls the MCP tool `manage_native_filters` with
`dashboard_id=D` and an
intentionally invalid request (for example, a `remove` list containing
filter ids that are
not on the dashboard, or `scope_chart_ids` including invalid chart ids) so
that validation
will fail. In the function body at
`superset/mcp_service/dashboard/tool/manage_native_filters.py:42-55`, the
code enters the
`"mcp.manage_native_filters.validation"` log context, loads the dashboard
via `dashboard =
DashboardDAO.find_by_id(request.dashboard_id)`, and if found, immediately
computes
`current_config = _current_native_filter_config(dashboard)` and
`dashboard_chart_ids =
[slc.id for slc in dashboard.slices]` before any ownership check.
3. `_current_native_filter_config` at
`superset/mcp_service/dashboard/tool/manage_native_filters.py:284-303` parses
`dashboard.json_metadata`, extracts `native_filter_configuration`, and
returns a list of
filter config dicts; then `_build_native_filters_payload(request,
current_config,
dashboard_chart_ids)` is invoked at lines 56-59 to validate the
user-supplied operations
against existing filter ids and chart ids. Inside
`_build_native_filters_payload` (lines
305-385), the function constructs `current_by_id` from the existing filter
configs and
uses `dashboard_chart_ids` to validate removals, updates, and scopes.
4. Because the request is invalid, `_build_native_filters_payload` raises
`_FilterValidationError` with messages that include internal details such as
existing
filter ids (`sorted(current_by_id)` when an update or remove target is
unknown) and chart
ids (`sorted(dashboard_chart_ids)` when `scope_chart_ids` include unknown
ids). This
exception is caught in `manage_native_filters` at
`superset/mcp_service/dashboard/tool/manage_native_filters.py:56-64`, where
the function
returns `ManageNativeFiltersResponse(dashboard_id=request.dashboard_id,
error=str(exc))`
to the caller. Since this all happens before the call to
`UpdateDashboardNativeFiltersCommand` at lines 66-69, any
`DashboardForbiddenError` raised
by the command for non-owners (caught at lines 97-106) is never triggered,
so a non-owner
with tool access can repeatedly probe dashboard id D and infer its native
filter ids and
chart ids from the validation error strings, instead of receiving a uniform
permission-denied response.
```
</details>
[](https://app.codeant.ai/fix-in-ide?tool=cursor&prompt_id=097fa69d735745ce9be8fd91469dbb43&service=github&base_url=https%3A%2F%2Fgithub.com&org=apache&repo=apache%2Fsuperset)
[](https://app.codeant.ai/fix-in-ide?tool=vscode-claude&prompt_id=097fa69d735745ce9be8fd91469dbb43&service=github&base_url=https%3A%2F%2Fgithub.com&org=apache&repo=apache%2Fsuperset)
*(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:** 423:438
**Comment:**
*Security: The tool reads `dashboard.json_metadata` and
`dashboard.slices` and runs detailed validation before ownership is enforced by
the command layer. This lets non-owners trigger validation errors that reveal
internal dashboard details (existing filter IDs, chart IDs, etc.) instead of
getting a straight permission-denied response. Perform an ownership check
immediately after loading the dashboard and before building payload/validation
output.
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>
<a
href='https://app.codeant.ai/feedback?pr_url=https%3A%2F%2Fgithub.com%2Fapache%2Fsuperset%2Fpull%2F40960&comment_hash=feb251f1e14078f69b33146561f0cd7adfef065c888b174c315f51ea28ea1d1f&reaction=like'>👍</a>
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href='https://app.codeant.ai/feedback?pr_url=https%3A%2F%2Fgithub.com%2Fapache%2Fsuperset%2Fpull%2F40960&comment_hash=feb251f1e14078f69b33146561f0cd7adfef065c888b174c315f51ea28ea1d1f&reaction=dislike'>👎</a>
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