run_realtime_report
Read-only
Generate realtime Google Analytics reports to analyze live user activity using dimensions, metrics, filters, ordering, and pagination.
Instructions
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.
minute_ranges: Optional minute ranges (max 2), e.g.
[{"name": "last5", "start_minutes_ago": 4, "end_minutes_ago": 0}].
start_minutes_ago can be at most 29. Omit for the last 30 minutes.
## 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 `minute_ranges`:
Example minute_ranges arguments:
1. The last 5 minutes:
[ {"start_minutes_ago": 4, "end_minutes_ago": 0, "name": "Last5Minutes"} ]
2. The last 5 minutes compared with the last 30 minutes:
[ {"start_minutes_ago": 4, "end_minutes_ago": 0, "name": "Last5Minutes"}, {"start_minutes_ago": 29, "end_minutes_ago": 0, "name": "Last30Minutes"} ]
3. A window that ends before now (15 to 29 minutes ago):
[ {"start_minutes_ago": 29, "end_minutes_ago": 15, "name": "Earlier15Minutes"} ]
Notes:
- At most 2 minute ranges are allowed.
- `start_minutes_ago` can be at most 29 for standard properties.
- Each range counts unique users within that window, so use a minute
range instead of summing rows of the `minutesAgo` dimension.
- When 2 ranges are given, the response includes a `dateRange`
dimension with each range's `name`.
- Omit `minute_ranges` to report on the last 30 minutes.
### 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.Tool call template
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"property_id": "<property_id>",
"dimensions": "<dimensions>",
"metrics": "<metrics>"
},
"name": "run_realtime_report"
}
}Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| offset | No | ||
| metrics | Yes | ||
| order_bys | No | ||
| dimensions | Yes | ||
| property_id | Yes | ||
| metric_filter | No | ||
| minute_ranges | No | ||
| dimension_filter | No | ||
| return_property_quota | No |