feat(mcp): add update_dataset_metric tool for editing saved dataset metrics#40975
feat(mcp): add update_dataset_metric tool for editing saved dataset metrics#40975msyavuz wants to merge 1 commit into
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| def resolve_dataset( | ||
| identifier: int | str, eager_options: list[Any] | None = None | ||
| ) -> Any | None: | ||
| """Resolve a dataset by int ID or UUID string. | ||
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| Replicates the identifier resolution logic from ModelGetInfoCore._find_object(). | ||
| """ | ||
| from superset.daos.dataset import DatasetDAO | ||
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| opts = eager_options or None | ||
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| if isinstance(identifier, int): | ||
| return DatasetDAO.find_by_id(identifier, query_options=opts) | ||
|
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| # Try parsing as int | ||
| try: | ||
| id_val = int(identifier) | ||
| return DatasetDAO.find_by_id(id_val, query_options=opts) | ||
| except (ValueError, TypeError): | ||
| pass |
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Suggestion: Restrict dataset lookup inputs to UUID-only identifiers and remove integer-ID resolution paths so this new helper does not expose internal numeric IDs through a public MCP-facing flow. [custom_rule]
Severity Level: Minor
Why it matters? 🤔
This new helper explicitly accepts and resolves integer IDs before UUIDs, which exposes internal numeric identifiers through a public MCP-facing lookup path.
That matches the custom rule about not introducing internal integer IDs when UUID-based identifiers are being used.
Fix in Cursor | Fix in VSCode Claude
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Prompt for AI Agent 🤖
This is a comment left during a code review.
**Path:** superset/mcp_service/dataset/dataset_utils.py
**Line:** 25:44
**Comment:**
*Custom Rule: Restrict dataset lookup inputs to UUID-only identifiers and remove integer-ID resolution paths so this new helper does not expose internal numeric IDs through a public MCP-facing flow.
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| def _metric_not_found_message(metrics: list[Any], identifier: int | str) -> str: | ||
| names = [m.metric_name for m in metrics] | ||
| msg = f"Metric '{identifier}' not found on this dataset." | ||
| if not names: | ||
| return f"{msg} This dataset has no saved metrics." | ||
| suggestions = difflib.get_close_matches(str(identifier), names, n=3, cutoff=0.6) | ||
| if suggestions: | ||
| return f"{msg} Did you mean: {', '.join(suggestions)}?" | ||
| return f"{msg} Available metrics: {', '.join(sorted(names))}." |
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Suggestion: Add a concise docstring to this newly added helper function so it is documented consistently with the other new functions in the file. [custom_rule]
Severity Level: Minor
Why it matters? 🤔
This is a newly added Python helper function and it does not include a docstring. The custom rule requires new functions and classes to be documented inline, so the suggestion accurately identifies a real violation.
Fix in Cursor | Fix in VSCode Claude
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Prompt for AI Agent 🤖
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**Path:** superset/mcp_service/dataset/tool/update_dataset_metric.py
**Line:** 62:70
**Comment:**
*Custom Rule: Add a concise docstring to this newly added helper function so it is documented consistently with the other new functions in the file.
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| def mcp_server(): | ||
| return mcp |
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Suggestion: Add an explicit return type annotation to this fixture function so new Python code is fully typed. [custom_rule]
Severity Level: Minor
Why it matters? 🤔
This is a new Python function in the PR and it has no return type annotation.
The rule requires new Python code to be fully typed, so this is a real violation.
Fix in Cursor | Fix in VSCode Claude
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**Path:** tests/unit_tests/mcp_service/dataset/tool/test_update_dataset_metric.py
**Line:** 39:40
**Comment:**
*Custom Rule: Add an explicit return type annotation to this fixture function so new Python code is fully typed.
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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| @pytest.fixture(autouse=True) | ||
| def mock_auth(): |
There was a problem hiding this comment.
Suggestion: Annotate the return type of this yielding fixture (for example as a generator type) to satisfy the full type-hint requirement for new functions. [custom_rule]
Severity Level: Minor
Why it matters? 🤔
This new fixture function lacks a return type annotation. Under the typing rule for new Python code, that omission is a genuine violation.
Fix in Cursor | Fix in VSCode Claude
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Prompt for AI Agent 🤖
This is a comment left during a code review.
**Path:** tests/unit_tests/mcp_service/dataset/tool/test_update_dataset_metric.py
**Line:** 44:44
**Comment:**
*Custom Rule: Annotate the return type of this yielding fixture (for example as a generator type) to satisfy the full type-hint requirement for new functions.
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| def make_metric( | ||
| metric_id=10, | ||
| metric_name="count", | ||
| uuid="a1b2c3d4-5678-90ab-cdef-1234567890ab", | ||
| **overrides, | ||
| ): |
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Suggestion: Add type hints for all parameters and the return value of this helper to comply with the fully typed new-code rule. [custom_rule]
Severity Level: Minor
Why it matters? 🤔
The helper is newly added and its parameters and return value are untyped.
That directly violates the requirement that new Python code be fully typed.
Fix in Cursor | Fix in VSCode Claude
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Prompt for AI Agent 🤖
This is a comment left during a code review.
**Path:** tests/unit_tests/mcp_service/dataset/tool/test_update_dataset_metric.py
**Line:** 56:61
**Comment:**
*Custom Rule: Add type hints for all parameters and the return value of this helper to comply with the fully typed new-code rule.
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| return metric | ||
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| def make_dataset(dataset_id=1, metrics=None): |
There was a problem hiding this comment.
Suggestion: Add parameter and return type annotations to this helper function so it meets the typing requirement for newly added functions. [custom_rule]
Severity Level: Minor
Why it matters? 🤔
This new helper function has untyped parameters and no return annotation, so it does violate the new-code typing rule.
Fix in Cursor | Fix in VSCode Claude
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Prompt for AI Agent 🤖
This is a comment left during a code review.
**Path:** tests/unit_tests/mcp_service/dataset/tool/test_update_dataset_metric.py
**Line:** 79:79
**Comment:**
*Custom Rule: Add parameter and return type annotations to this helper function so it meets the typing requirement for newly added functions.
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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| dataset_id: int | str = Field( | ||
| ..., | ||
| description="Dataset identifier — numeric ID or UUID string. " | ||
| "Use list_datasets to find valid IDs.", | ||
| ) |
There was a problem hiding this comment.
Suggestion: Restrict this request identifier to UUID-only input (or a dedicated UUID field) so the new public API does not continue accepting internal integer IDs. [custom_rule]
Severity Level: Minor
Why it matters? 🤔
This is a new public API request field that explicitly accepts numeric internal IDs in addition to UUIDs. The rule says new public API identifiers should not expose or depend on internal integer IDs when UUID-based identifiers are being added or changed. This is a real violation.
Fix in Cursor | Fix in VSCode Claude
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Prompt for AI Agent 🤖
This is a comment left during a code review.
**Path:** superset/mcp_service/dataset/schemas.py
**Line:** 520:524
**Comment:**
*Custom Rule: Restrict this request identifier to UUID-only input (or a dedicated UUID field) so the new public API does not continue accepting internal integer IDs.
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| metric: int | str = Field( | ||
| ..., | ||
| description="Metric to update — numeric metric ID, metric UUID, or " | ||
| "metric_name (e.g. 'sum_revenue'). Numeric strings are treated as IDs. " | ||
| "Use get_dataset_info to discover a dataset's saved metrics.", |
There was a problem hiding this comment.
Suggestion: Remove numeric metric ID support from this API identifier and require UUID (or non-internal name-only lookup) to avoid exposing or depending on internal integer IDs in the new interface. [custom_rule]
Severity Level: Minor
Why it matters? 🤔
This request schema explicitly allows a numeric metric ID and even treats numeric strings as IDs, which exposes and depends on internal integer identifiers in a new public interface. That matches the rule violation.
Fix in Cursor | Fix in VSCode Claude
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Prompt for AI Agent 🤖
This is a comment left during a code review.
**Path:** superset/mcp_service/dataset/schemas.py
**Line:** 525:529
**Comment:**
*Custom Rule: Remove numeric metric ID support from this API identifier and require UUID (or non-internal name-only lookup) to avoid exposing or depending on internal integer IDs in the new interface.
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| id: int | None = Field(None, description="Metric ID") | ||
| uuid: str | None = Field(None, description="Metric UUID") |
There was a problem hiding this comment.
Suggestion: Drop the integer metric ID from the response model and expose only the UUID-based identifier for this newly added metric detail payload. [custom_rule]
Severity Level: Minor
Why it matters? 🤔
The response model includes an integer metric ID alongside a UUID field. Since this is a newly added public API payload and a UUID identifier is being introduced, exposing the internal integer ID violates the rule.
Fix in Cursor | Fix in VSCode Claude
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Prompt for AI Agent 🤖
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**Path:** superset/mcp_service/dataset/schemas.py
**Line:** 592:593
**Comment:**
*Custom Rule: Drop the integer metric ID from the response model and expose only the UUID-based identifier for this newly added metric detail payload.
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| class UpdateDatasetMetricResponse(BaseModel): | ||
| """Response schema for update_dataset_metric.""" | ||
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| dataset_id: int | None = Field(None, description="Dataset ID") |
There was a problem hiding this comment.
Suggestion: Replace this integer dataset identifier field with a UUID-based public identifier in the new response schema to avoid exposing internal numeric IDs. [custom_rule]
Severity Level: Minor
Why it matters? 🤔
This response schema exposes an internal integer dataset ID in a new public API response. That is exactly the kind of identifier the rule says to avoid when UUID-based identifiers are being added or changed.
Fix in Cursor | Fix in VSCode Claude
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Prompt for AI Agent 🤖
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**Path:** superset/mcp_service/dataset/schemas.py
**Line:** 604:604
**Comment:**
*Custom Rule: Replace this integer dataset identifier field with a UUID-based public identifier in the new response schema to avoid exposing internal numeric IDs.
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
Codecov Report❌ Patch coverage is Additional details and impacted files@@ Coverage Diff @@
## master #40975 +/- ##
==========================================
- Coverage 64.30% 64.27% -0.03%
==========================================
Files 2657 2659 +2
Lines 144059 144188 +129
Branches 33216 33228 +12
==========================================
+ Hits 92639 92684 +45
- Misses 49798 49880 +82
- Partials 1622 1624 +2
Flags with carried forward coverage won't be shown. Click here to find out more. ☔ View full report in Codecov by Harness. 🚀 New features to boost your workflow:
|
| symbolPosition: Literal["prefix", "suffix"] | None = Field( # noqa: N815 | ||
| None, description="Where to render the symbol relative to the value." |
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Suggestion: Restricting currency.symbolPosition to only "prefix"/"suffix" is incompatible with the existing dataset update contract, which accepts arbitrary non-empty strings (and legacy data may contain other values). This can cause valid update requests or serialization of existing metric currency payloads to fail with validation errors. Align this field type with the broader Superset schema behavior instead of enforcing a strict enum here. [api mismatch]
Severity Level: Major ⚠️
- ❌ update_dataset_metric fails for metrics with custom symbolPosition
- ⚠️ MCP clients see unexpected validation or internal errorsSteps of Reproduction ✅
1. Observe the existing REST dataset update contract in
`superset/datasets/schemas.py:17-20`, where
`DatasetMetricCurrencyPutSchema.symbolPosition` is defined as
`fields.String(validate=Length(1, 128))` with no enum restriction, and in the generated
OpenAPI docs (`docs/static/resources/openapi.json:5205-5208`) describing `symbolPosition`
as a generic string field. This confirms existing APIs accept arbitrary non-empty strings
(not just `"prefix"`/`"suffix"`).
2. Note that metric currency values are stored in the database via `CurrencyType` in
`superset/models/sql_types/base.py:67-86`, which parses legacy string values into dicts
using `parse_currency_string` without constraining `symbolPosition`. As a result, existing
datasets can legitimately contain arbitrary `currency={"symbol": "USD", "symbolPosition":
"before"}` or other non-enum values.
3. Call the MCP tool `update_dataset_metric` (entrypoint
`superset/mcp_service/dataset/tool/update_dataset_metric.py:113-229`) against any dataset
whose metric has such a currency dict (e.g. created previously via the REST API or UI), by
sending a minimal request that does not touch currency: `{"request": {"dataset_id": 1,
"metric": "count", "expression": "COUNT(1)"}}`. The tool loads the dataset metrics (lines
167-179) and locates the target metric (lines 179-183).
4. After `UpdateDatasetCommand.run()` returns the updated dataset (lines 207-213), the
tool serializes the metric via `_serialize_metric` at `update_dataset_metric.py:73-100`.
That calls `MetricCurrency.model_validate(currency)` when `currency` is a dict (lines
93-95); however `MetricCurrency.symbolPosition` is declared as `Literal["prefix",
"suffix"]` in `superset/mcp_service/dataset/schemas.py:35-43`, so any existing metric with
`symbolPosition` not equal to `"prefix"` or `"suffix"` causes a
`pydantic.ValidationError`. This bubbles into the broad `except Exception` block at lines
253-258, which logs `"Unexpected error updating dataset metric"` and re-raises, causing
the MCP tool call to fail with an internal error even though the underlying dataset update
may have succeeded.Fix in Cursor | Fix in VSCode Claude
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**Path:** superset/mcp_service/dataset/schemas.py
**Line:** 510:511
**Comment:**
*Api Mismatch: Restricting `currency.symbolPosition` to only `"prefix"`/`"suffix"` is incompatible with the existing dataset update contract, which accepts arbitrary non-empty strings (and legacy data may contain other values). This can cause valid update requests or serialization of existing metric currency payloads to fail with validation errors. Align this field type with the broader Superset schema behavior instead of enforcing a strict enum here.
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| class DatasetMetricDetail(SqlMetricInfo): | ||
| """Full saved-metric details, including identifiers.""" | ||
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| id: int | None = Field(None, description="Metric ID") | ||
| uuid: str | None = Field(None, description="Metric UUID") | ||
| metric_type: str | None = Field(None, description="Metric type") | ||
| currency: MetricCurrency | None = Field( | ||
| None, description="Currency formatting configuration" | ||
| ) | ||
| warning_text: str | None = Field(None, description="Warning text") |
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Suggestion: The response model for updated metrics omits the extra property even though the request allows updating it and the tool advertises returning the metric after update. This makes the response incomplete for one of the supported update fields and breaks callers that expect to read back the updated extra value. Add extra to the metric detail response schema so response content matches supported updates. [incomplete implementation]
Severity Level: Major ⚠️
- ⚠️ update_dataset_metric response omits updated extra metadata
- ⚠️ Callers cannot confirm saved metric extra changesSteps of Reproduction ✅
1. Inspect the request schema for the MCP tool in
`superset/mcp_service/dataset/schemas.py:46-92`. `UpdateDatasetMetricRequest` includes
`extra: str | None = Field(None, description="JSON-encoded string with extra metric
metadata.")` (lines 88-92) and `UPDATABLE_METRIC_FIELDS` (lines 20-32) includes `"extra"`,
so callers are explicitly allowed to update `extra`.
2. Inspect the response-side metric model `DatasetMetricDetail` in the same file at
`superset/mcp_service/dataset/schemas.py:120-129`. It subclasses `SqlMetricInfo` (which
defines `metric_name`, `verbose_name`, `expression`, `description`, `d3format` at lines
41-50) and adds `id`, `uuid`, `metric_type`, `currency`, and `warning_text` (lines
123-129), but does not define an `extra` field.
3. Examine the tool implementation in
`superset/mcp_service/dataset/tool/update_dataset_metric.py:73-100`. `_serialize_metric()`
builds a `DatasetMetricDetail` from the ORM metric, setting `id`, `uuid`, `metric_name`,
`verbose_name`, `expression`, `description`, `d3format`, `metric_type`, `currency`, and
`warning_text`, but never includes `metric.extra` when constructing the response object.
4. From an MCP client, call the tool `update_dataset_metric` (entrypoint
`update_dataset_metric.py:113-229`) with a payload that only updates `extra`, for example:
`{"request": {"dataset_id": 1, "metric": "count", "extra": "{\"foo\": \"bar\"}"}}`. The
request validates and `UpdateDatasetMetricRequest.updates()` (lines 94-103) includes
`"extra"` in the metrics payload sent to `UpdateDatasetCommand`. After the update, the
tool returns `UpdateDatasetMetricResponse.metric` as a `DatasetMetricDetail` (lines
220-223), but because `DatasetMetricDetail` has no `extra` field and `_serialize_metric()`
never passes it through, the JSON response lacks any `metric["extra"]` field. Thus callers
that just updated `extra` cannot read back the new value via this tool, despite the schema
advertising that the metric is returned "after the update".Fix in Cursor | Fix in VSCode Claude
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Prompt for AI Agent 🤖
This is a comment left during a code review.
**Path:** superset/mcp_service/dataset/schemas.py
**Line:** 589:598
**Comment:**
*Incomplete Implementation: The response model for updated metrics omits the `extra` property even though the request allows updating it and the tool advertises returning the metric after update. This makes the response incomplete for one of the supported update fields and breaks callers that expect to read back the updated `extra` value. Add `extra` to the metric detail response schema so response content matches supported updates.
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[bot]](https://avatars.githubusercontent.com/in/1061978?s=60&v=4){kind=small}
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Code Review Agent Run #513598
Actionable Suggestions - 1
-
superset/mcp_service/dataset/dataset_utils.py - 1
- Missing unit tests for new utility · Line 25-50
Additional Suggestions - 2
-
tests/unit_tests/mcp_service/dataset/tool/test_update_dataset_metric.py - 1
-
Missing error path test coverage · Line 253-378The test suite covers DatasetNotFoundError, DatasetForbiddenError, and DatasetInvalidError, but does not test DatasetUpdateFailedError which is handled at update_dataset_metric.py lines 248-252. Adding coverage ensures complete error path validation.
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superset/mcp_service/app.py - 1
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Documentation field name mismatch · Line 166-166The documentation says 'label' but the actual updatable field is named 'verbose_name' per the schema definition. Update the description to say 'verbose_name' for consistency.
Code suggestion
--- a/superset/mcp_service/app.py +++ b/superset/mcp_service/app.py @@ -163,7 +163,7 @@ Dataset Management: - list_datasets: List datasets with advanced filters (1-based pagination) - get_dataset_info: Get detailed dataset information by ID (includes columns/metrics) - create_virtual_dataset: Save a SQL query as a virtual dataset for charting (requires write access) -- update_dataset_metric: Update a saved metric on a dataset — expression, name, label, format (requires dataset ownership) +- update_dataset_metric: Update a saved metric on a dataset — expression, name, verbose_name, format (requires dataset ownership) - query_dataset: Query a dataset using its semantic layer (saved metrics, dimensions, filters) without needing a saved chart
-
Filtered by Review Rules
Bito filtered these suggestions based on rules created automatically for your feedback. Manage rules.
-
tests/unit_tests/mcp_service/dataset/tool/test_update_dataset_metric.py - 1
- Missing pytest-asyncio decorator · Line 340-340
-
superset/mcp_service/dataset/schemas.py - 1
- Missing validation_alias for symbolPosition · Line 510-512
Review Details
-
Files reviewed - 8 · Commit Range:
2311482..2311482- superset/mcp_service/app.py
- superset/mcp_service/dataset/dataset_utils.py
- superset/mcp_service/dataset/schemas.py
- superset/mcp_service/dataset/tool/__init__.py
- superset/mcp_service/dataset/tool/query_dataset.py
- superset/mcp_service/dataset/tool/update_dataset_metric.py
- tests/unit_tests/mcp_service/dataset/tool/test_query_dataset.py
- tests/unit_tests/mcp_service/dataset/tool/test_update_dataset_metric.py
-
Files skipped - 0
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| def resolve_dataset( | ||
| identifier: int | str, eager_options: list[Any] | None = None | ||
| ) -> Any | None: | ||
| """Resolve a dataset by int ID or UUID string. | ||
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| Replicates the identifier resolution logic from ModelGetInfoCore._find_object(). | ||
| """ | ||
| from superset.daos.dataset import DatasetDAO | ||
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| opts = eager_options or None | ||
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| if isinstance(identifier, int): | ||
| return DatasetDAO.find_by_id(identifier, query_options=opts) | ||
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| # Try parsing as int | ||
| try: | ||
| id_val = int(identifier) | ||
| return DatasetDAO.find_by_id(id_val, query_options=opts) | ||
| except (ValueError, TypeError): | ||
| pass | ||
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| # Try UUID | ||
| if _is_uuid(str(identifier)): | ||
| return DatasetDAO.find_by_id(identifier, id_column="uuid", query_options=opts) | ||
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| return None |
There was a problem hiding this comment.
The resolve_dataset function is new but lacks dedicated unit tests. Per BITO.md rule [11730], new tools/functions require comprehensive unit tests covering success paths, error scenarios, and edge cases. Tests should be placed in tests/unit_tests/mcp_service/dataset/ following existing MCP test patterns.
Code Review Run #513598
Should Bito avoid suggestions like this for future reviews? (Manage Rules)
- Yes, avoid them
1 participant
SUMMARY
MCP exposes saved dataset metrics read-only via
get_dataset_info, but there is no write path — editing a metric's SQL expression, name, label, or format requires the UI. This adds anupdate_dataset_metrictool: given a dataset (ID/UUID) and a metric (ID/UUID/name), it partially updates the metric's properties (expression,metric_name,verbose_name,description,d3format,metric_type,currency,warning_text,extra).Design notes:
DatasetDAO.update_metricsis full-replacement (metrics omitted from the array are deleted), which is an easy foot-gun for LLM callers. The tool rebuilds the full metrics list internally — untouched metrics as{id, metric_name}stubs, the target with only the explicitly-passed properties merged on top — so it can never create or delete metrics.UpdateDatasetCommand, which enforces dataset ownership and validates name uniqueness and the SQL expression (validate_stored_expression).exclude_unsetsemantics distinguish "not provided" (keep stored value) from explicitnull(clear it).query_datasetinto a shareddataset_utils.resolve_dataset.Example call:
{"dataset_id": 123, "metric": "sum_revenue", "expression": "SUM(net_revenue)", "d3format": "$,.2f"}BEFORE/AFTER SCREENSHOTS OR ANIMATED GIF
N/A — MCP tool, no UI.
TESTING INSTRUCTIONS
pytest tests/unit_tests/mcp_service/dataset/(97 tests)update_dataset_metricwith a dataset ID and metric name, confirm the change in the dataset editor and that other metrics are untouched.ADDITIONAL INFORMATION
update_dataset_metric