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noviq-ai

Google Analytics MCP Server

by noviq-ai
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Python
Remote

run_realtime_report

Execute real-time Google Analytics reports to monitor current user activity, using customizable dimensions, metrics, and filters for immediate insights.

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
property_idYes
dimensionsYes
metricsYes
dimension_filterNo
metric_filterNo
order_bysNo
limitNo
offsetNo
return_property_quotaNo

Tool Definition Quality

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses behaviors such as pagination (limit/offset), independent application of dimension and metric filters with complex limitations, and notes that realtime reports cannot use custom metrics. It also provides quota management guidance. While it doesn't explicitly state read-only behavior, the nature of a report implies it. Overall, it is transparent about key behavioral aspects.

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, but it is well-structured with sections, hints, and examples. However, the 'Notes' on filter combinations are repeated verbatim for both dimension_filter and metric_filter, which is redundant. Some trimming could improve conciseness while maintaining clarity.

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

Completeness4/5

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

Given the complexity (9 parameters, nested objects, no output schema), the description is comprehensive. It covers parameter constraints, usage examples, and complex limitations. Missing are explicit error handling details and output format, but these are partially addressed by links to the API documentation. Overall, it is thorough and helps the agent use the tool correctly.

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?

The input schema has 0% description coverage, so the description fully compensates. It explains each parameter in detail, including accepted formats for property_id, allowed types for dimensions/metrics (with links to schemas), structure of filter expressions and order_bys with examples, and constraints for limit/offset. This goes far beyond the bare schema.

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 the tool runs a Google Analytics Data API realtime report. It specifies the resource (realtime report) and the action (runs), and the title reinforces 'realtime' to distinguish from the sibling 'run_report' which implies standard reports. The purpose is unambiguous.

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

Usage Guidelines3/5

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

The description provides rich context for using the tool, including parameter constraints and examples. However, it does not explicitly state when to use this tool versus the sibling 'run_report' or when not to use it. The usage is implied by the 'realtime' designation, but no direct comparison or exclusion criteria are given.

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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