aminghadersohi commented on code in PR #41611: URL: https://github.com/apache/superset/pull/41611#discussion_r3508232700
########## superset/mcp_service/semantic_layer/tool/list_metrics.py: ########## @@ -0,0 +1,267 @@ +# 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: list_metrics + +Unified metric discovery across built-in datasets and external semantic views. +""" + +import logging +from typing import Any + +from fastmcp import Context +from superset_core.mcp.decorators import tool, ToolAnnotations + +from superset.extensions import event_logger +from superset.mcp_service.privacy import ( + DATA_MODEL_METADATA_ERROR_TYPE, + requires_data_model_metadata_access, + user_can_view_data_model_metadata, +) +from superset.mcp_service.semantic_layer.schemas import ( + DimensionInfo, + ListMetricsRequest, + MetricInfo, + MetricList, + SemanticLayerError, +) + +logger = logging.getLogger(__name__) + + +def _matches_search(text: str | None, search: str) -> bool: + if not text: + return False + return search.lower() in text.lower() + + +def _builtin_compatible_dims(dataset: Any) -> list[DimensionInfo]: + """Return groupby-enabled columns as compatible dimensions for a built-in metric.""" + return [ + DimensionInfo( + name=col.column_name, + verbose_name=col.verbose_name or None, + description=col.description or None, + type=col.type or None, + is_dttm=bool(col.is_dttm), + groupby=bool(col.groupby), + filterable=bool(col.filterable), + source="builtin", + ) + for col in dataset.columns + if col.groupby + ] + + +def _collect_builtin_metrics(request: ListMetricsRequest) -> list[MetricInfo]: + """Collect metrics from built-in SqlaTable datasets.""" + from sqlalchemy.orm import subqueryload + + from superset.connectors.sqla.models import SqlaTable + from superset.daos.dataset import DatasetDAO + + with event_logger.log_context(action="mcp.list_metrics.builtin_query"): + if request.dataset_id is not None: + dataset = DatasetDAO.find_by_id( + request.dataset_id, + query_options=[ + subqueryload(SqlaTable.columns), + subqueryload(SqlaTable.metrics), + ], + ) + datasets = [dataset] if dataset else [] + else: + datasets = DatasetDAO.find_all() + + results: list[MetricInfo] = [] + for dataset in datasets: + compat_dims = ( + _builtin_compatible_dims(dataset) + if request.include_compatible_dimensions + else [] + ) + for metric in dataset.metrics: + name = metric.metric_name or "" + desc = metric.description or "" + if request.search and not ( + _matches_search(name, request.search) + or _matches_search(desc, request.search) + ): + continue + results.append( + MetricInfo( + name=name, + verbose_name=metric.verbose_name or None, + description=desc or None, + expression=metric.expression or None, + d3format=metric.d3format or None, + warning_text=metric.warning_text or None, + source="builtin", + dataset_id=dataset.id, + dataset_name=dataset.table_name, + compatible_dimensions=compat_dims, + ) + ) + return results + + +async def _collect_external_metrics( + request: ListMetricsRequest, + ctx: Context, +) -> list[MetricInfo]: + """Collect metrics from external SemanticView models.""" + from superset.daos.semantic_layer import SemanticViewDAO + + with event_logger.log_context(action="mcp.list_metrics.external_query"): + if request.view_id is not None: + view = SemanticViewDAO.find_by_id(request.view_id) + views = [view] if view else [] + else: + views = SemanticViewDAO.find_all() + + await ctx.debug("Found %d semantic views to scan for metrics" % len(views)) + + results: list[MetricInfo] = [] + for view in views: + try: + raw_metrics = view.metrics + raw_cols = view.columns if request.include_compatible_dimensions else [] + compat_dims = [ + DimensionInfo( + name=col.column_name, + verbose_name=col.verbose_name, + description=col.description, + type=col.type, + is_dttm=col.is_dttm, + groupby=col.groupby, + filterable=col.filterable, + source="external", + ) + for col in raw_cols + ] + for metric in raw_metrics: + name = metric.metric_name or "" + desc = metric.description or "" + if request.search and not ( + _matches_search(name, request.search) + or _matches_search(desc, request.search) + ): + continue + results.append( + MetricInfo( + name=name, + description=desc or None, + expression=metric.expression or None, + source="external", + view_id=view.id, + view_name=view.name, + compatible_dimensions=compat_dims, + ) + ) + except Exception as exc: # noqa: BLE001 + # External registry may be empty in OSS — degrade gracefully + await ctx.warning( + "Could not load metrics for view id=%s: %s" % (view.id, str(exc)) + ) + return results + + +@tool( + tags=["data", "semantic"], + class_permission_name="Dataset", + annotations=ToolAnnotations( + title="List metrics", + readOnlyHint=True, + destructiveHint=False, + ), +) +@requires_data_model_metadata_access +async def list_metrics( + request: ListMetricsRequest | None = None, + ctx: Context | None = None, +) -> MetricList | SemanticLayerError: + """List available metrics across built-in datasets and external semantic views. + + This is the primary entry point for semantic layer exploration. Returns a + unified list of metrics from all data sources the current user can access, + with compatible dimensions included inline. + + Workflow: + 1. list_metrics -> discover available metrics and their compatible dimensions + 2. get_table -> query data using chosen metrics and dimensions + + Use ``search`` to filter by metric name or description. Use ``dataset_id`` + or ``view_id`` to scope to a specific data source. + + Example: + ```json + {"search": "revenue", "include_compatible_dimensions": true, "page": 1} + ``` + """ + if ctx is None: + raise RuntimeError("FastMCP context is required for list_metrics") + + request = request or ListMetricsRequest() + + await ctx.info( + "Listing metrics: search=%s, dataset_id=%s, view_id=%s, page=%s" + % (request.search, request.dataset_id, request.view_id, request.page) + ) Review Comment: Fixed — added an explicit mutual-exclusion check: `list_metrics` now returns a `ValidationError` when both `dataset_id` and `view_id` are provided, instead of silently returning an empty list. See `list_metrics.py` lines 250-254. ########## superset/mcp_service/semantic_layer/tool/list_metrics.py: ########## @@ -0,0 +1,267 @@ +# 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: list_metrics + +Unified metric discovery across built-in datasets and external semantic views. +""" + +import logging +from typing import Any + +from fastmcp import Context +from superset_core.mcp.decorators import tool, ToolAnnotations + +from superset.extensions import event_logger +from superset.mcp_service.privacy import ( + DATA_MODEL_METADATA_ERROR_TYPE, + requires_data_model_metadata_access, + user_can_view_data_model_metadata, +) +from superset.mcp_service.semantic_layer.schemas import ( + DimensionInfo, + ListMetricsRequest, + MetricInfo, + MetricList, + SemanticLayerError, +) + +logger = logging.getLogger(__name__) + + +def _matches_search(text: str | None, search: str) -> bool: + if not text: + return False + return search.lower() in text.lower() + + +def _builtin_compatible_dims(dataset: Any) -> list[DimensionInfo]: + """Return groupby-enabled columns as compatible dimensions for a built-in metric.""" + return [ + DimensionInfo( + name=col.column_name, + verbose_name=col.verbose_name or None, + description=col.description or None, + type=col.type or None, + is_dttm=bool(col.is_dttm), + groupby=bool(col.groupby), + filterable=bool(col.filterable), + source="builtin", + ) + for col in dataset.columns + if col.groupby + ] + + +def _collect_builtin_metrics(request: ListMetricsRequest) -> list[MetricInfo]: + """Collect metrics from built-in SqlaTable datasets.""" + from sqlalchemy.orm import subqueryload + + from superset.connectors.sqla.models import SqlaTable + from superset.daos.dataset import DatasetDAO + + with event_logger.log_context(action="mcp.list_metrics.builtin_query"): + if request.dataset_id is not None: + dataset = DatasetDAO.find_by_id( + request.dataset_id, + query_options=[ + subqueryload(SqlaTable.columns), + subqueryload(SqlaTable.metrics), + ], + ) + datasets = [dataset] if dataset else [] + else: + datasets = DatasetDAO.find_all() + + results: list[MetricInfo] = [] + for dataset in datasets: + compat_dims = ( + _builtin_compatible_dims(dataset) + if request.include_compatible_dimensions + else [] + ) + for metric in dataset.metrics: + name = metric.metric_name or "" + desc = metric.description or "" + if request.search and not ( + _matches_search(name, request.search) + or _matches_search(desc, request.search) + ): + continue + results.append( + MetricInfo( + name=name, + verbose_name=metric.verbose_name or None, + description=desc or None, + expression=metric.expression or None, + d3format=metric.d3format or None, + warning_text=metric.warning_text or None, + source="builtin", + dataset_id=dataset.id, + dataset_name=dataset.table_name, + compatible_dimensions=compat_dims, + ) + ) + return results + + +async def _collect_external_metrics( + request: ListMetricsRequest, + ctx: Context, +) -> list[MetricInfo]: + """Collect metrics from external SemanticView models.""" + from superset.daos.semantic_layer import SemanticViewDAO + + with event_logger.log_context(action="mcp.list_metrics.external_query"): + if request.view_id is not None: + view = SemanticViewDAO.find_by_id(request.view_id) + views = [view] if view else [] + else: + views = SemanticViewDAO.find_all() + + await ctx.debug("Found %d semantic views to scan for metrics" % len(views)) + + results: list[MetricInfo] = [] + for view in views: + try: + raw_metrics = view.metrics + raw_cols = view.columns if request.include_compatible_dimensions else [] Review Comment: Fixed — external `SemanticView` paths now call `view.raise_for_access()` before reading metrics/columns. For the list-all case, we switched from a per-row Python check to `SemanticViewDAO.find_accessible()`, which filters at the SQL level (mirrors the permission filter in `DatasourceDAO.build_semantic_view_query`), so unauthorized views are never loaded in the first place. See `list_metrics.py` lines 143-157. ########## superset/mcp_service/semantic_layer/tool/list_metrics.py: ########## @@ -0,0 +1,267 @@ +# 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: list_metrics + +Unified metric discovery across built-in datasets and external semantic views. +""" + +import logging +from typing import Any + +from fastmcp import Context +from superset_core.mcp.decorators import tool, ToolAnnotations + +from superset.extensions import event_logger +from superset.mcp_service.privacy import ( + DATA_MODEL_METADATA_ERROR_TYPE, + requires_data_model_metadata_access, + user_can_view_data_model_metadata, +) +from superset.mcp_service.semantic_layer.schemas import ( + DimensionInfo, + ListMetricsRequest, + MetricInfo, + MetricList, + SemanticLayerError, +) + +logger = logging.getLogger(__name__) + + +def _matches_search(text: str | None, search: str) -> bool: + if not text: + return False + return search.lower() in text.lower() + + +def _builtin_compatible_dims(dataset: Any) -> list[DimensionInfo]: + """Return groupby-enabled columns as compatible dimensions for a built-in metric.""" + return [ + DimensionInfo( + name=col.column_name, + verbose_name=col.verbose_name or None, + description=col.description or None, + type=col.type or None, + is_dttm=bool(col.is_dttm), + groupby=bool(col.groupby), + filterable=bool(col.filterable), + source="builtin", + ) + for col in dataset.columns + if col.groupby + ] + + +def _collect_builtin_metrics(request: ListMetricsRequest) -> list[MetricInfo]: + """Collect metrics from built-in SqlaTable datasets.""" + from sqlalchemy.orm import subqueryload + + from superset.connectors.sqla.models import SqlaTable + from superset.daos.dataset import DatasetDAO + + with event_logger.log_context(action="mcp.list_metrics.builtin_query"): + if request.dataset_id is not None: + dataset = DatasetDAO.find_by_id( + request.dataset_id, + query_options=[ + subqueryload(SqlaTable.columns), + subqueryload(SqlaTable.metrics), + ], + ) + datasets = [dataset] if dataset else [] + else: + datasets = DatasetDAO.find_all() + + results: list[MetricInfo] = [] + for dataset in datasets: + compat_dims = ( + _builtin_compatible_dims(dataset) + if request.include_compatible_dimensions + else [] + ) + for metric in dataset.metrics: + name = metric.metric_name or "" + desc = metric.description or "" + if request.search and not ( + _matches_search(name, request.search) + or _matches_search(desc, request.search) + ): + continue + results.append( + MetricInfo( + name=name, + verbose_name=metric.verbose_name or None, + description=desc or None, + expression=metric.expression or None, + d3format=metric.d3format or None, + warning_text=metric.warning_text or None, + source="builtin", + dataset_id=dataset.id, + dataset_name=dataset.table_name, + compatible_dimensions=compat_dims, + ) + ) + return results + + +async def _collect_external_metrics( + request: ListMetricsRequest, + ctx: Context, +) -> list[MetricInfo]: + """Collect metrics from external SemanticView models.""" + from superset.daos.semantic_layer import SemanticViewDAO + + with event_logger.log_context(action="mcp.list_metrics.external_query"): + if request.view_id is not None: + view = SemanticViewDAO.find_by_id(request.view_id) + views = [view] if view else [] + else: + views = SemanticViewDAO.find_all() + + await ctx.debug("Found %d semantic views to scan for metrics" % len(views)) + + results: list[MetricInfo] = [] + for view in views: + try: + raw_metrics = view.metrics + raw_cols = view.columns if request.include_compatible_dimensions else [] + compat_dims = [ + DimensionInfo( + name=col.column_name, + verbose_name=col.verbose_name, + description=col.description, + type=col.type, + is_dttm=col.is_dttm, + groupby=col.groupby, + filterable=col.filterable, + source="external", + ) + for col in raw_cols + ] + for metric in raw_metrics: + name = metric.metric_name or "" + desc = metric.description or "" + if request.search and not ( + _matches_search(name, request.search) + or _matches_search(desc, request.search) + ): + continue + results.append( + MetricInfo( + name=name, + description=desc or None, + expression=metric.expression or None, + source="external", + view_id=view.id, + view_name=view.name, + compatible_dimensions=compat_dims, + ) + ) + except Exception as exc: # noqa: BLE001 + # External registry may be empty in OSS — degrade gracefully + await ctx.warning( + "Could not load metrics for view id=%s: %s" % (view.id, str(exc)) + ) + return results + + +@tool( + tags=["data", "semantic"], + class_permission_name="Dataset", + annotations=ToolAnnotations( + title="List metrics", + readOnlyHint=True, + destructiveHint=False, + ), +) +@requires_data_model_metadata_access +async def list_metrics( + request: ListMetricsRequest | None = None, + ctx: Context | None = None, +) -> MetricList | SemanticLayerError: + """List available metrics across built-in datasets and external semantic views. + + This is the primary entry point for semantic layer exploration. Returns a + unified list of metrics from all data sources the current user can access, + with compatible dimensions included inline. + + Workflow: + 1. list_metrics -> discover available metrics and their compatible dimensions + 2. get_table -> query data using chosen metrics and dimensions + + Use ``search`` to filter by metric name or description. Use ``dataset_id`` + or ``view_id`` to scope to a specific data source. + + Example: + ```json + {"search": "revenue", "include_compatible_dimensions": true, "page": 1} + ``` + """ + if ctx is None: + raise RuntimeError("FastMCP context is required for list_metrics") + + request = request or ListMetricsRequest() + + await ctx.info( + "Listing metrics: search=%s, dataset_id=%s, view_id=%s, page=%s" + % (request.search, request.dataset_id, request.view_id, request.page) + ) + + if not user_can_view_data_model_metadata(): + await ctx.warning("Metric listing blocked by data-model privacy controls") + return SemanticLayerError.create( + error="You don't have permission to access dataset details for your role.", + error_type=DATA_MODEL_METADATA_ERROR_TYPE, + ) + + try: + all_metrics: list[MetricInfo] = [] + + if request.view_id is None: + all_metrics.extend(_collect_builtin_metrics(request)) + await ctx.debug("Collected %d built-in metrics" % len(all_metrics)) + + if request.dataset_id is None: + external = await _collect_external_metrics(request, ctx) + all_metrics.extend(external) + await ctx.debug("Collected %d external metrics" % len(external)) + + total_count = len(all_metrics) + total_pages = max(1, (total_count + request.page_size - 1) // request.page_size) + start = (request.page - 1) * request.page_size + page_metrics = all_metrics[start : start + request.page_size] + + await ctx.info( + "Metrics listed: total=%d, page=%d/%d, returned=%d" + % (total_count, request.page, total_pages, len(page_metrics)) + ) + + return MetricList( + metrics=page_metrics, + total_count=total_count, + page=request.page, + page_size=request.page_size, + total_pages=total_pages, + ) + + except Exception as exc: + logger.exception( + "Unexpected error in list_metrics: %s: %s", type(exc).__name__, str(exc) + ) + await ctx.error("Unexpected error: %s: %s" % (type(exc).__name__, str(exc))) + raise Review Comment: Fixed — the outer `except Exception` in `list_metrics` now returns a typed `SemanticLayerError(error_type="InternalError")` instead of re-raising, consistent with the other semantic-layer tools. See `list_metrics.py` lines 286-294. ########## superset/mcp_service/semantic_layer/tool/list_metrics.py: ########## @@ -0,0 +1,267 @@ +# 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: list_metrics + +Unified metric discovery across built-in datasets and external semantic views. +""" + +import logging +from typing import Any + +from fastmcp import Context +from superset_core.mcp.decorators import tool, ToolAnnotations + +from superset.extensions import event_logger +from superset.mcp_service.privacy import ( + DATA_MODEL_METADATA_ERROR_TYPE, + requires_data_model_metadata_access, + user_can_view_data_model_metadata, +) +from superset.mcp_service.semantic_layer.schemas import ( + DimensionInfo, + ListMetricsRequest, + MetricInfo, + MetricList, + SemanticLayerError, +) + +logger = logging.getLogger(__name__) + + +def _matches_search(text: str | None, search: str) -> bool: + if not text: + return False + return search.lower() in text.lower() + + +def _builtin_compatible_dims(dataset: Any) -> list[DimensionInfo]: + """Return groupby-enabled columns as compatible dimensions for a built-in metric.""" + return [ + DimensionInfo( + name=col.column_name, + verbose_name=col.verbose_name or None, + description=col.description or None, + type=col.type or None, + is_dttm=bool(col.is_dttm), + groupby=bool(col.groupby), + filterable=bool(col.filterable), + source="builtin", + ) + for col in dataset.columns + if col.groupby + ] + + +def _collect_builtin_metrics(request: ListMetricsRequest) -> list[MetricInfo]: + """Collect metrics from built-in SqlaTable datasets.""" + from sqlalchemy.orm import subqueryload + + from superset.connectors.sqla.models import SqlaTable + from superset.daos.dataset import DatasetDAO + + with event_logger.log_context(action="mcp.list_metrics.builtin_query"): + if request.dataset_id is not None: + dataset = DatasetDAO.find_by_id( + request.dataset_id, + query_options=[ + subqueryload(SqlaTable.columns), + subqueryload(SqlaTable.metrics), + ], + ) + datasets = [dataset] if dataset else [] + else: + datasets = DatasetDAO.find_all() + + results: list[MetricInfo] = [] + for dataset in datasets: + compat_dims = ( + _builtin_compatible_dims(dataset) + if request.include_compatible_dimensions + else [] + ) + for metric in dataset.metrics: + name = metric.metric_name or "" + desc = metric.description or "" + if request.search and not ( + _matches_search(name, request.search) + or _matches_search(desc, request.search) + ): + continue + results.append( + MetricInfo( + name=name, + verbose_name=metric.verbose_name or None, + description=desc or None, + expression=metric.expression or None, + d3format=metric.d3format or None, + warning_text=metric.warning_text or None, + source="builtin", + dataset_id=dataset.id, + dataset_name=dataset.table_name, + compatible_dimensions=compat_dims, + ) + ) + return results + + +async def _collect_external_metrics( + request: ListMetricsRequest, + ctx: Context, +) -> list[MetricInfo]: + """Collect metrics from external SemanticView models.""" + from superset.daos.semantic_layer import SemanticViewDAO + + with event_logger.log_context(action="mcp.list_metrics.external_query"): + if request.view_id is not None: + view = SemanticViewDAO.find_by_id(request.view_id) + views = [view] if view else [] + else: + views = SemanticViewDAO.find_all() + + await ctx.debug("Found %d semantic views to scan for metrics" % len(views)) + + results: list[MetricInfo] = [] + for view in views: + try: + raw_metrics = view.metrics + raw_cols = view.columns if request.include_compatible_dimensions else [] + compat_dims = [ + DimensionInfo( + name=col.column_name, + verbose_name=col.verbose_name, + description=col.description, + type=col.type, + is_dttm=col.is_dttm, + groupby=col.groupby, + filterable=col.filterable, + source="external", + ) + for col in raw_cols + ] + for metric in raw_metrics: + name = metric.metric_name or "" + desc = metric.description or "" + if request.search and not ( + _matches_search(name, request.search) + or _matches_search(desc, request.search) + ): + continue + results.append( + MetricInfo( + name=name, + description=desc or None, + expression=metric.expression or None, + source="external", + view_id=view.id, + view_name=view.name, + compatible_dimensions=compat_dims, + ) + ) + except Exception as exc: # noqa: BLE001 + # External registry may be empty in OSS — degrade gracefully + await ctx.warning( + "Could not load metrics for view id=%s: %s" % (view.id, str(exc)) + ) + return results + + +@tool( + tags=["data", "semantic"], + class_permission_name="Dataset", + annotations=ToolAnnotations( + title="List metrics", + readOnlyHint=True, + destructiveHint=False, + ), +) +@requires_data_model_metadata_access +async def list_metrics( + request: ListMetricsRequest | None = None, + ctx: Context | None = None, +) -> MetricList | SemanticLayerError: Review Comment: Added unit tests for `list_metrics` (happy path, mutual exclusion, privacy check, search filter) in the same commit, and just followed up with unit tests for `get_table`, `get_compatible_dimensions`, and `get_compatible_metrics` as well, so all four new tools now have coverage under `tests/unit_tests/mcp_service/semantic_layer/tool/`. -- This is an automated message from the Apache Git Service. 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