google-analytics-mcp

Run Funnel Report

run_funnel_report

Run a funnel analysis to see how many users complete each step in a sequence, identifying drop-off points in Google Analytics 4.

Instructions

Run a funnel analysis report for a Google Analytics 4 property.

WARNING: EXPERIMENTAL: This uses the GA4 Data API v1alpha endpoint which is unstable and may break or change without notice.

A funnel shows how many users complete each step in a sequence — e.g. homepage -> product page -> add to cart -> purchase.

Each step in steps must be a dict with:

"name": human-readable step label (e.g. "Homepage")
"filterExpression": a GA4 funnel filter expression dict

STEP FORMAT EXAMPLES:

Page path step: { "name": "Homepage", "filterExpression": { "funnelFieldFilter": { "fieldName": "pagePath", "stringFilter": {"matchType": "EXACT", "value": "/"} } } }

Event step: { "name": "Purchase", "filterExpression": { "funnelEventFilter": { "eventName": "purchase" } } }

Input Schema

TableJSON Schema

Name	Required	Description
`steps`	Yes	List of funnel step dicts (minimum 2 steps)
`end_date`	Yes	End date in YYYY-MM-DD format
`start_date`	Yes	Start date in YYYY-MM-DD format
`property_id`	Yes	Google Analytics 4 property ID (numeric, e.g., "123456789")
`breakdown_dimension`	No	Optional dimension to break down funnel by (e.g. "deviceCategory")

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 21, 2026

TDQS

A3.8/5.0

Behavior3/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose the experimental, unstable nature of the GA4 Data API v1alpha endpoint, which is valuable. However, it does not mention permissions, rate limits, output behavior, or whether the operation has side effects, leaving several behavioral aspects unaddressed.

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 longer than average, but the added length is justified by the experimental warning and detailed step-format examples, both of which are essential for correct use. Information is front-loaded with purpose and warning before examples, and there is minimal filler.

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?

For a tool with an output schema and fully described input parameters, the description covers the trickiest part—how to construct funnel step filters. It leaves some contextual gaps, such as explicit guidance on selecting this over run_report, but it is largely complete given the available structured information.

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?

Although schema coverage is 100%, the description adds substantial meaning beyond the schema by specifying the required structure of each step dict and providing full JSON examples for page-path and event steps. It also clarifies the minimum step count, which is not present in the 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 opens with a specific verb and resource: 'Run a funnel analysis report for a Google Analytics 4 property.' It clearly defines what a funnel report does and includes a concrete example sequence, distinguishing it from the generic sibling run_report and other analytics tools.

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

Usage Guidelines2/5

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

The description implies use for funnel analysis but does not explicitly state when to use this tool versus alternatives like run_report, nor does it mention when not to use it. An agent must infer the use case from the name and examples rather than receiving direct routing guidance.

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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