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Google Analytics MCP Server

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Run a Google Analytics realtime report using the Data API

run_realtime_report

Run a Google Analytics realtime report to retrieve current activity data for a property, with options to filter, sort, and paginate results.

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
NameRequiredDescriptionDefault
limitNo
offsetNo
metricsYes
order_bysNo
dimensionsYes
property_idYes
metric_filterNo
dimension_filterNo
return_property_quotaNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses that the report is realtime, includes pagination, filter independence limitations, and optional quota return. However, it does not explicitly state whether the tool is read-only or any authentication requirements, though these are implicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very long and includes repeated notes (e.g., the same filter limitation appears for both dimension_filter and metric_filter). While well-structured, it could be more concise. Every sentence adds some value, but the length impacts readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of 9 parameters, nested objects, and filter limitations, the description is thorough. It covers parameter formats, constraints, examples, API limitations, and links to external resources. The presence of an output schema does not reduce the need for description, and it meets that need fully.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate with detailed parameter explanations. It provides format, constraints, examples, and links for all parameters, including dimensions, metrics, filters, order_bys, and pagination. This fully compensates for the lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool runs a Google Analytics Data API realtime report, specifying the resource (realtime report) and the type of data (realtime). It distinguishes itself from sibling tools like 'run_report', which handles non-realtime reports.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

While the description does not explicitly state 'use this for realtime data only' or provide alternatives, the name and title make it clear. The extensive hints and notes about filter limitations guide appropriate usage. The lack of explicit when-not guidance slightly reduces the score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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