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 ...
Part of the Google Analytics server.
Free to start. No card required.
AI agents invoke run_realtime_report to trigger processes or run actions in Google Analytics. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
run_realtime_report can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"run_realtime_report": {
"limits": [
{
"counter": "run_realtime_report_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full Google Analytics policy for all 7 tools.
These attack patterns abuse exactly the kind of access run_realtime_report gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
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.. It is categorised as a Execute tool in the Google Analytics MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Google Analytics MCP server in PolicyLayer and add a rule for run_realtime_report: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Google Analytics. Nothing to install.
run_realtime_report is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the run_realtime_report rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for run_realtime_report. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
run_realtime_report is provided by the Google Analytics MCP server (google-analytics). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 7 Google Analytics tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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