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ga4-mcp

by Walma-Labs
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TypeScript
Hybrid

run_funnel_report

Run Google Analytics 4 funnel reports to measure step-by-step user progression, segment by dimension, and see next actions.

Instructions

Runs a Google Analytics Data API funnel report (https://developers.google.com/analytics/devguides/reporting/data/v1/funnels).

Returns funnel_table (progression through the steps) and funnel_visualization, both flattened to one object per row.

funnel_steps

Each step is {name, filter_expression} or the shorthand {name, event}. Example funnel_steps configurations:

  1. Shorthand event step: {"name":"Session start","event":"session_start"}

  2. Several events with OR: {"name":"First open/visit","filter_expression":{"or_group":{"expressions":[{"funnel_event_filter":{"event_name":"first_open"}},{"funnel_event_filter":{"event_name":"first_visit"}}]}}}

  3. Field filter for organic traffic: {"name":"Organic visitors","filter_expression":{"funnel_field_filter":{"field_name":"firstUserMedium","string_filter":{"match_type":"CONTAINS","case_sensitive":false,"value":"organic"}}}}

  4. Event with a parameter filter (value > 50): {"name":"Add to cart (value > 50)","filter_expression":{"funnel_event_filter":{"event_name":"add_to_cart","funnel_parameter_filter_expression":{"funnel_parameter_filter":{"event_parameter_name":"value","numeric_filter":{"operation":"GREATER_THAN","value":{"double_value":50}}}}}}}

  5. AND condition (page view on a path): {"name":"Home page view","filter_expression":{"and_group":{"expressions":[{"funnel_event_filter":{"event_name":"page_view"}},{"funnel_field_filter":{"field_name":"pageLocation","string_filter":{"match_type":"CONTAINS","value":"/"}}}]}}}

A typical e-commerce funnel: [ {"name":"First open/visit","filter_expression":{"or_group":{"expressions":[{"funnel_event_filter":{"event_name":"first_open"}},{"funnel_event_filter":{"event_name":"first_visit"}}]}}}, {"name":"Organic visitors","filter_expression":{"funnel_field_filter":{"field_name":"firstUserMedium","string_filter":{"match_type":"CONTAINS","case_sensitive":false,"value":"organic"}}}}, {"name":"Session start","filter_expression":{"funnel_event_filter":{"event_name":"session_start"}}}, {"name":"Screen/Page view","filter_expression":{"or_group":{"expressions":[{"funnel_event_filter":{"event_name":"screen_view"}},{"funnel_event_filter":{"event_name":"page_view"}}]}}}, {"name":"Purchase","filter_expression":{"or_group":{"expressions":[{"funnel_event_filter":{"event_name":"purchase"}},{"funnel_event_filter":{"event_name":"in_app_purchase"}}]}}} ]

funnel_breakdown

Segment the funnel by a dimension: {"breakdown_dimension": "deviceCategory"}. Common: deviceCategory, country, operatingSystem, browser.

funnel_next_action

What users do after completing or dropping off: {"next_action_dimension": "eventName", "limit": 5}. Common: eventName, pagePath.

segments

Data API Segment objects (https://developers.google.com/analytics/devguides/reporting/data/v1/funnels#segments).

date_ranges

Example date_ranges 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': [ {"start_date":"30daysAgo","end_date":"yesterday","name":"Previous30Days"} ]

  4. Multiple date ranges (adds a 'dateRange' column to every row): [ {"start_date":"2025-01-01","end_date":"2025-01-31","name":"Jan2025"}, {"start_date":"2025-02-01","end_date":"2025-02-28","name":"Feb2025"} ]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
segmentsNo
date_rangesNoDate ranges: [{start_date, end_date, name?}]. Dates are YYYY-MM-DD, 'today', 'yesterday' or 'NdaysAgo'.
property_idYesThe Google Analytics property ID: a number, or 'properties/' followed by a number. Use get_account_summaries to find it.
funnel_stepsYes
funnel_breakdownNo
funnel_next_actionNo
return_property_quotaNoInclude the property's quota usage in the response.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It discloses that results are flattened to one object per row and that multiple date_ranges add a dateRange column, but it does not describe permissions, quota behavior, error conditions, or whether the operation is read-only.

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

Conciseness4/5

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

The description is well organized with purpose and return values front-loaded, followed by parameter-specific sections. It is long and includes multiple verbose examples, but for a complex nested funnel_steps schema this length is largely justified.

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 complex nested input, lack of annotations, and absence of an output schema, the description provides strong invocation guidance and partial return-value context. It still omits deeper behavioral details such as auth requirements, quota limits, and error handling.

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 only 43%, so the description must compensate and does so extensively. It provides detailed examples for funnel_steps, funnel_breakdown, funnel_next_action, segments, and date_ranges, including shorthand event steps, filter_expression shapes, AND/OR groups, and relative date syntax.

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

Purpose4/5

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

The description states a specific verb and resource: 'Runs a Google Analytics Data API funnel report.' It clearly identifies the report type and mentions the returned artifacts, funnel_table and funnel_visualization, but it does not explicitly distinguish this tool from sibling tools such as run_report or run_conversions_report.

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?

Usage is implied by the funnel-specific examples and configuration sections, but the description never states when to choose this tool over run_report or run_conversions_report. There are no explicit when-to-use or when-not-to-use rules.

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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