run_report
Execute a Google Analytics report by specifying property, date ranges, dimensions, metrics, and filters to retrieve targeted analytics data.
Instructions
Runs a Google Analytics Data API report.
Note that the reference docs at
https://developers.google.com/analytics/devguides/reporting/data/v1/rest/v1beta
all use camelCase field names, but field names passed to this method should
be in snake_case since the tool is using the protocol buffers (protobuf)
format. The protocol buffers for the Data API are available at
https://github.com/googleapis/googleapis/tree/master/google/analytics/data/v1beta.
Args:
property_id: The Google Analytics property ID. Accepted formats are:
- A number
- A string consisting of 'properties/' followed by a number
date_ranges: A list of date ranges
(https://developers.google.com/analytics/devguides/reporting/data/v1/rest/v1beta/DateRange)
to include in the report.
dimensions: A list of dimensions to include in the report.
metrics: A list of metrics to include in the report.
dimension_filter: A Data API FilterExpression
(https://developers.google.com/analytics/devguides/reporting/data/v1/rest/v1beta/FilterExpression)
to apply to the dimensions. Don't use this for filtering metrics. Use
metric_filter instead. The `field_name` in a `dimension_filter` must
be a dimension, as defined in the `get_standard_dimensions` and
`get_dimensions` tools.
metric_filter: A Data API FilterExpression
(https://developers.google.com/analytics/devguides/reporting/data/v1/rest/v1beta/FilterExpression)
to apply to the metrics. Don't use this for filtering dimensions. Use
dimension_filter instead. The `field_name` in a `metric_filter` must
be a metric, as defined in the `get_standard_metrics` and
`get_metrics` tools.
order_bys: A list of Data API OrderBy
(https://developers.google.com/analytics/devguides/reporting/data/v1/rest/v1beta/OrderBy)
objects to apply to the dimensions and metrics.
limit: The maximum number of rows to return in each response. Value must
be a positive integer <= 250,000. Used to paginate through large
reports, following the guide at
https://developers.google.com/analytics/devguides/reporting/data/v1/basics#pagination.
offset: The row count of the start row. The first row is counted as row
0. Used to paginate through large
reports, following the guide at
https://developers.google.com/analytics/devguides/reporting/data/v1/basics#pagination.
currency_code: The currency code to use for currency values. Must be in
ISO4217 format, such as "AED", "USD", "JPY". If the field is empty, the
report uses the property's default currency.
return_property_quota: Whether to return property quota in the response.
## Hints for arguments
Here are some hints that outline the expected format and requirements
for arguments.
### Hints for `dimensions`
The `dimensions` list must consist solely of either of the following:
1. Standard dimensions defined in the HTML table at
https://developers.google.com/analytics/devguides/reporting/data/v1/api-schema#dimensions.
These dimensions are available to *every* property.
2. Custom dimensions for the `property_id`. Use the
`get_custom_dimensions_and_metrics` tool to retrieve the list of
custom dimensions for a property.
### Hints for `metrics`
The `metrics` list must consist solely of either of the following:
1. Standard metrics defined in the HTML table at
https://developers.google.com/analytics/devguides/reporting/data/v1/api-schema#metrics.
These metrics are available to *every* property.
2. Custom metrics for the `property_id`. Use the
`get_custom_dimensions_and_metrics` tool to retrieve the list of
custom metrics for a property.
### Hints for `date_ranges`:
Example date_range arguments:
1. A single date range:
[ {"start_date": "2025-01-01", "end_date": "2025-01-31", "name": "Jan2025"} ]
2. A relative date range using 'yesterday' and 'today':
[ {"start_date": "yesterday", "end_date": "today", "name": "YesterdayAndToday"} ]
3. A relative date range using 'NdaysAgo' and 'today':
[ {"start_date": "30daysAgo", "end_date": "yesterday", "name": "Previous30Days"}]
4. Multiple date ranges:
[ {"start_date": "2025-01-01", "end_date": "2025-01-31", "name": "Jan2025"}, {"start_date": "2025-02-01", "end_date": "2025-02-28", "name": "Feb2025"} ]
### Hints for `dimension_filter`:
Example dimension_filter arguments:
1. A simple filter:
{"filter": {"field_name": "eventName", "string_filter": {"match_type": 2, "value": "add", "case_sensitive": false}}}
2. A NOT filter:
{"not_expression": {"filter": {"field_name": "eventName", "string_filter": {"match_type": 2, "value": "add", "case_sensitive": false}}}}
3. An empty value filter:
{"filter": {"field_name": "source", "empty_filter": {}}}
4. An AND group filter:
{"and_group": {"expressions": [{"filter": {"field_name": "sourceMedium", "string_filter": {"match_type": 1, "value": "google / cpc", "case_sensitive": false}}}, {"filter": {"field_name": "eventName", "in_list_filter": {"values": ["first_visit", "purchase", "add_to_cart"], "case_sensitive": true}}}]}}
5. An OR group filter:
{"or_group": {"expressions": [{"filter": {"field_name": "sourceMedium", "string_filter": {"match_type": 1, "value": "google / cpc", "case_sensitive": false}}}, {"filter": {"field_name": "eventName", "in_list_filter": {"values": ["first_visit", "purchase", "add_to_cart"], "case_sensitive": true}}}]}}
Notes:
The API applies the dimension_filter and metric_filter
independently. As a result, some complex combinations of dimension and
metric filters are not possible in a single report request.
For example, you can't create a `dimension_filter` and `metric_filter`
combination for the following condition:
(
(eventName = "page_view" AND eventCount > 100)
OR
(eventName = "join_group" AND eventCount < 50)
)
This isn't possible because there's no way to apply the condition
"eventCount > 100" only to the data with eventName of "page_view", and
the condition "eventCount < 50" only to the data with eventName of
"join_group".
More generally, you can't define a `dimension_filter` and `metric_filter`
for:
(
((dimension condition D1) AND (metric condition M1))
OR
((dimension condition D2) AND (metric condition M2))
)
If you have complex conditions like this, either:
a) Run a single report that applies a subset of the conditions that
the API supports as well as the data needed to perform filtering of the
API response on the client side. For example, for the condition:
(
(eventName = "page_view" AND eventCount > 100)
OR
(eventName = "join_group" AND eventCount < 50)
)
You could run a report that filters only on:
eventName one of "page_view" or "join_group"
and include the eventCount metric, then filter the API response on the
client side to apply the different metric filters for the different
events.
or
b) Run a separate report for each combination of dimension condition and
metric condition. For the example above, you'd run one report for the
combination of (D1 AND M1), and another report for the combination of
(D2 AND M2).
Try to run fewer reports (option a) if possible. However, if running
fewer reports results in excessive quota usage for the API, use option
b. More information on quota usage is at
https://developers.google.com/analytics/blog/2023/data-api-quota-management.
### Hints for `metric_filter`:
Example metric_filter arguments:
1. A simple filter:
{"filter": {"field_name": "eventCount", "numeric_filter": {"operation": 4, "value": {"int64_value": "10"}}}}
2. A NOT filter:
{"not_expression": {"filter": {"field_name": "eventCount", "numeric_filter": {"operation": 4, "value": {"int64_value": "10"}}}}}
3. An empty value filter:
{"filter": {"field_name": "purchaseRevenue", "empty_filter": {}}}
4. An AND group filter:
{"and_group": {"expressions": [{"filter": {"field_name": "eventCount", "numeric_filter": {"operation": 4, "value": {"int64_value": "10"}}}}, {"filter": {"field_name": "purchaseRevenue", "between_filter": {"from_value": {"double_value": 10.0}, "to_value": {"double_value": 25.0}}}}]}}
5. An OR group filter:
{"or_group": {"expressions": [{"filter": {"field_name": "eventCount", "numeric_filter": {"operation": 4, "value": {"int64_value": "10"}}}}, {"filter": {"field_name": "purchaseRevenue", "between_filter": {"from_value": {"double_value": 10.0}, "to_value": {"double_value": 25.0}}}}]}}
Notes:
The API applies the dimension_filter and metric_filter
independently. As a result, some complex combinations of dimension and
metric filters are not possible in a single report request.
For example, you can't create a `dimension_filter` and `metric_filter`
combination for the following condition:
(
(eventName = "page_view" AND eventCount > 100)
OR
(eventName = "join_group" AND eventCount < 50)
)
This isn't possible because there's no way to apply the condition
"eventCount > 100" only to the data with eventName of "page_view", and
the condition "eventCount < 50" only to the data with eventName of
"join_group".
More generally, you can't define a `dimension_filter` and `metric_filter`
for:
(
((dimension condition D1) AND (metric condition M1))
OR
((dimension condition D2) AND (metric condition M2))
)
If you have complex conditions like this, either:
a) Run a single report that applies a subset of the conditions that
the API supports as well as the data needed to perform filtering of the
API response on the client side. For example, for the condition:
(
(eventName = "page_view" AND eventCount > 100)
OR
(eventName = "join_group" AND eventCount < 50)
)
You could run a report that filters only on:
eventName one of "page_view" or "join_group"
and include the eventCount metric, then filter the API response on the
client side to apply the different metric filters for the different
events.
or
b) Run a separate report for each combination of dimension condition and
metric condition. For the example above, you'd run one report for the
combination of (D1 AND M1), and another report for the combination of
(D2 AND M2).
Try to run fewer reports (option a) if possible. However, if running
fewer reports results in excessive quota usage for the API, use option
b. More information on quota usage is at
https://developers.google.com/analytics/blog/2023/data-api-quota-management.
### Hints for `order_bys`:
Example order_bys arguments:
1. Order by ascending 'eventName':
[ {"dimension": {"dimension_name": "eventName", "order_type": 1}, "desc": false} ]
2. Order by descending 'eventName', ignoring case:
[ {"dimension": {"dimension_name": "campaignName", "order_type": 2}, "desc": true} ]
3. Order by ascending 'audienceId':
[ {"dimension": {"dimension_name": "audienceId", "order_type": 3}, "desc": false} ]
4. Order by descending 'eventCount':
[ {"metric": {"metric_name": "eventValue"}, "desc": true} ]
5. Order by ascending 'eventCount':
[ {"metric": {"metric_name": "eventCount"}, "desc": false} ]
6. Combination of dimension and metric order bys:
[
{"dimension": {"dimension_name": "eventName", "order_type": 1}, "desc": false},
{"metric": {"metric_name": "eventValue"}, "desc": true},
]
7. Order by multiple dimensions and metrics:
[
{"dimension": {"dimension_name": "eventName", "order_type": 1}, "desc": false},
{"dimension": {"dimension_name": "audienceId", "order_type": 3}, "desc": false},
{"metric": {"metric_name": "eventValue"}, "desc": true},
]
The dimensions and metrics in order_bys must also be present in the report
request's "dimensions" and "metrics" arguments, respectively.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| property_id | Yes | ||
| date_ranges | Yes | ||
| dimensions | Yes | ||
| metrics | Yes | ||
| dimension_filter | No | ||
| metric_filter | No | ||
| order_bys | No | ||
| limit | No | ||
| offset | No | ||
| currency_code | No | ||
| return_property_quota | No |
Implementation Reference
- The main handler function for the 'run_report' tool. It accepts parameters like property_id, date_ranges, dimensions, metrics, filters, order_bys, limit, offset, currency_code, and return_property_quota. It constructs a RunReportRequest protobuf, calls the Data API client, and returns the response as a dict.
async def run_report( property_id: int | str, date_ranges: List[Dict[str, Any]], dimensions: List[str], metrics: List[str], dimension_filter: Dict[str, Any] = None, metric_filter: Dict[str, Any] = None, order_bys: List[Dict[str, Any]] = None, limit: int = None, offset: int = None, currency_code: str = None, return_property_quota: bool = False, ) -> Dict[str, Any]: """Runs a Google Analytics Data API report. Note that the reference docs at https://developers.google.com/analytics/devguides/reporting/data/v1/rest/v1beta all use camelCase field names, but field names passed to this method should be in snake_case since the tool is using the protocol buffers (protobuf) format. The protocol buffers for the Data API are available at https://github.com/googleapis/googleapis/tree/master/google/analytics/data/v1beta. Args: property_id: The Google Analytics property ID. Accepted formats are: - A number - A string consisting of 'properties/' followed by a number date_ranges: A list of date ranges (https://developers.google.com/analytics/devguides/reporting/data/v1/rest/v1beta/DateRange) to include in the report. dimensions: A list of dimensions to include in the report. metrics: A list of metrics to include in the report. dimension_filter: A Data API FilterExpression (https://developers.google.com/analytics/devguides/reporting/data/v1/rest/v1beta/FilterExpression) to apply to the dimensions. Don't use this for filtering metrics. Use metric_filter instead. The `field_name` in a `dimension_filter` must be a dimension, as defined in the `get_standard_dimensions` and `get_dimensions` tools. metric_filter: A Data API FilterExpression (https://developers.google.com/analytics/devguides/reporting/data/v1/rest/v1beta/FilterExpression) to apply to the metrics. Don't use this for filtering dimensions. Use dimension_filter instead. The `field_name` in a `metric_filter` must be a metric, as defined in the `get_standard_metrics` and `get_metrics` tools. order_bys: A list of Data API OrderBy (https://developers.google.com/analytics/devguides/reporting/data/v1/rest/v1beta/OrderBy) objects to apply to the dimensions and metrics. limit: The maximum number of rows to return in each response. Value must be a positive integer <= 250,000. Used to paginate through large reports, following the guide at https://developers.google.com/analytics/devguides/reporting/data/v1/basics#pagination. offset: The row count of the start row. The first row is counted as row 0. Used to paginate through large reports, following the guide at https://developers.google.com/analytics/devguides/reporting/data/v1/basics#pagination. currency_code: The currency code to use for currency values. Must be in ISO4217 format, such as "AED", "USD", "JPY". If the field is empty, the report uses the property's default currency. return_property_quota: Whether to return property quota in the response. """ request = data_v1beta.RunReportRequest( property=construct_property_rn(property_id), dimensions=[ data_v1beta.Dimension(name=dimension) for dimension in dimensions ], metrics=[data_v1beta.Metric(name=metric) for metric in metrics], date_ranges=[data_v1beta.DateRange(dr) for dr in date_ranges], return_property_quota=return_property_quota, ) if dimension_filter: request.dimension_filter = data_v1beta.FilterExpression( dimension_filter ) if metric_filter: request.metric_filter = data_v1beta.FilterExpression(metric_filter) if order_bys: request.order_bys = [ data_v1beta.OrderBy(order_by) for order_by in order_bys ] if limit: request.limit = limit if offset: request.offset = offset if currency_code: request.currency_code = currency_code def _sync_call(): return create_data_api_client().run_report(request) response = await asyncio.to_thread(_sync_call) return proto_to_dict(response) - Generates the tool description for 'run_report', including hints for dimensions, metrics, date_ranges, dimension_filter, metric_filter, and order_bys arguments by delegating to metadata helper functions.
def _run_report_description() -> str: """Returns the description for the `run_report` tool.""" return f""" {run_report.__doc__} ## Hints for arguments Here are some hints that outline the expected format and requirements for arguments. ### Hints for `dimensions` The `dimensions` list must consist solely of either of the following: 1. Standard dimensions defined in the HTML table at https://developers.google.com/analytics/devguides/reporting/data/v1/api-schema#dimensions. These dimensions are available to *every* property. 2. Custom dimensions for the `property_id`. Use the `get_custom_dimensions_and_metrics` tool to retrieve the list of custom dimensions for a property. ### Hints for `metrics` The `metrics` list must consist solely of either of the following: 1. Standard metrics defined in the HTML table at https://developers.google.com/analytics/devguides/reporting/data/v1/api-schema#metrics. These metrics are available to *every* property. 2. Custom metrics for the `property_id`. Use the `get_custom_dimensions_and_metrics` tool to retrieve the list of custom metrics for a property. ### Hints for `date_ranges`: {get_date_ranges_hints()} ### Hints for `dimension_filter`: {get_dimension_filter_hints()} ### Hints for `metric_filter`: {get_metric_filter_hints()} ### Hints for `order_bys`: {get_order_bys_hints()} """ - analytics_mcp/coordinator.py:54-55 (registration)Wraps the run_report function as a FunctionTool and assigns it a custom description. The tool is then included in the tools list and the MCP tool map for invocation.
run_report_with_description = FunctionTool(run_report) run_report_with_description.description = _run_report_description() - analytics_mcp/coordinator.py:127-133 (schema)Explicitly marks required fields (property_id, date_ranges, dimensions, metrics) on the input schema for the 'run_report' MCP tool to guide the LLM.
if tool.name == "run_report": tool.inputSchema["required"] = [ "property_id", "date_ranges", "dimensions", "metrics", ] - analytics_mcp/tools/client.py:73-96 (helper)Factory function that creates and returns the BetaAnalyticsDataClient used by run_report to execute the API call.
def create_data_api_client() -> data_v1beta.BetaAnalyticsDataClient: """Returns the Google Analytics Data API client.""" with _client_lock: return data_v1beta.BetaAnalyticsDataClient( client_info=_CLIENT_INFO, credentials=_get_credentials() ) def create_admin_alpha_api_client() -> ( admin_v1alpha.AnalyticsAdminServiceClient ): """Returns the Google Analytics Admin API (alpha) client.""" with _client_lock: return admin_v1alpha.AnalyticsAdminServiceClient( client_info=_CLIENT_INFO, credentials=_get_credentials() ) def create_data_api_alpha_client() -> data_v1alpha.AlphaAnalyticsDataClient: """Returns the Google Analytics Data API (Alpha) client.""" with _client_lock: return data_v1alpha.AlphaAnalyticsDataClient( client_info=_CLIENT_INFO, credentials=_get_credentials() )