| get_account_summaries | Retrieves information about the user's Google Analytics accounts and properties. |
| list_google_ads_links | Returns a list of links to Google Ads accounts for a property. Args:
property_id: The Google Analytics property ID. Accepted formats are:
- A number
- A string consisting of 'properties/' followed by a number |
| get_property_details | Returns details about a property.
Args:
property_id: The Google Analytics property ID. Accepted formats are:
- A number
- A string consisting of 'properties/' followed by a number |
| list_property_annotations | Returns annotations for a property. Annotations are a feature that allows you to leave notes on GA4 for specific dates or periods.
They are typically used to record service releases, marketing campaign launches or changes,
and rapid traffic increases or decreases due to external factors. Args:
property_id: The Google Analytics property ID. Accepted formats are:
- A number
- A string consisting of 'properties/' followed by a number |
| get_custom_dimensions_and_metrics | Returns the property's custom dimensions and metrics. Args:
property_id: The Google Analytics property ID. Accepted formats are:
- A number
- A string consisting of 'properties/' followed by a number |
| run_report | 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.
|
| run_realtime_report | Runs a Google Analytics Data API realtime report.
See
https://developers.google.com/analytics/devguides/reporting/data/v1/realtime-basics
for more information. Args:
property_id: The Google Analytics property ID. Accepted formats are:
- A number
- A string consisting of 'properties/' followed by a number
dimensions: A list of dimensions to include in the report. Dimensions must be realtime dimensions.
metrics: A list of metrics to include in the report. Metrics must be realtime metrics.
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.
For more information about the expected format of this argument, see
the run_report_dimension_filter_hints tool.
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.
For more information about the expected format of this argument, see
the run_report_metric_filter_hints tool.
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.
For more information about the expected format of this argument, see
the run_report_order_bys_hints tool.
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.
return_property_quota: Whether to return realtime 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. Realtime standard dimensions defined in the HTML table at
https://developers.google.com/analytics/devguides/reporting/data/v1/realtime-api-schema#dimensions.
These dimensions are available to *every* property.
2. User-scoped custom dimensions for the `property_id`. Use the
`get_custom_dimensions_and_metrics` tool to retrieve the list of
custom dimensions for a property, and look for the custom
dimensions with an `apiName` that begins with "customUser:".
### Hints for `metrics`
The `metrics` list must consist solely of the Realtime standard
metrics defined in the HTML table at
https://developers.google.com/analytics/devguides/reporting/data/v1/realtime-api-schema#metrics.
These metrics are available to *every* property.
Realtime reports can't use custom metrics.
### 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.
|
