run_realtime_report

Generate real-time Google Analytics reports to monitor current user activity, filter data by dimensions and metrics, and analyze immediate website performance.

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.


      ## 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.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`property_id`	Yes
`dimensions`	Yes
`metrics`	Yes
`dimension_filter`	No
`metric_filter`	No
`order_bys`	No
`limit`	No
`offset`	No
`return_property_quota`	No

Implementation Reference

analytics_mcp/tools/reporting/realtime.py:80-163 (handler)

The main handler function that executes the 'run_realtime_report' tool logic by constructing a RunRealtimeReportRequest and calling the Data API client's run_realtime_report method.

async def run_realtime_report(
    property_id: int | str,
    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,
    return_property_quota: bool = False,
) -> Dict[str, Any]:
    """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.
    """
    request = data_v1beta.RunRealtimeReportRequest(
        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],
        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

    response = await create_data_api_client().run_realtime_report(request)
    return proto_to_dict(response)

analytics_mcp/tools/reporting/realtime.py:169-173 (registration)
Registration of the 'run_realtime_report' tool using mcp.add_tool, with a dynamic title and description.
```
mcp.add_tool(
    run_realtime_report,
    title="Run a Google Analytics realtime report using the Data API",
    description=_run_realtime_report_description(),
)
```

analytics_mcp/tools/reporting/realtime.py:34-77 (helper)

Helper function that generates a detailed description for the 'run_realtime_report' tool, incorporating the function's docstring and hints for arguments from other metadata tools.

def _run_realtime_report_description() -> str:
    """Returns the description for the `run_realtime_report` tool."""
    return f"""
          {run_realtime_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.  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`:
          {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()}

"""

Google Analytics MCP Server