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christophertanenso

mcp-marketing-analytics

ga4_user_metrics

Get a summary of key user metrics from GA4: total users, new users, sessions, page views, bounce rate, avg session duration, and engagement rate. Optionally compare with previous period.

Instructions

Get a summary of key user metrics: total users, new users, sessions, page views, bounce rate, avg session duration, and engagement rate. Optionally compare with the previous period.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endDateNoEnd datetoday
startDateNoStart date28daysAgo
propertyIdNoGA4 Property ID. Use ga4_list_accounts to find available properties.
comparePreviousPeriodNoIf true, also fetches the previous period of the same length for comparison
Behavior3/5

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

With no annotations provided, the description carries the burden of disclosing behavior. It indicates a read-only operation via the verb 'Get' and lists the metrics returned, which is a positive signal. However, it does not mention potential limitations like data sampling, response shape, or whether the comparison adds separate fields, which would enhance transparency for a tool without an output schema.

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

Conciseness5/5

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

The description is a single, well-structured sentence that front-loads the purpose and lists the exact metrics. It is concise, scannable, and contains no filler or redundant details, earning a top score.

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

Completeness3/5

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

Given that there is no output schema, the description partially compensates by enumerating the returned metrics. However, it does not explain the response format (e.g., single row vs. table), how the previous-period comparison is represented, or any prerequisites like the property ID. While the tool is relatively simple, the missing output structure and comparison details leave some gaps for an agent.

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

Parameters3/5

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

The input schema already covers all four parameters with descriptions (100% coverage), so the description does not need to add parameter-level detail. It does provide context by explaining that 'compare with the previous period' relates to the comparePreviousPeriod flag, but this adds no new syntactic or semantic info beyond the schema. Baseline 3 is appropriate.

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 retrieves a summary of key user metrics, listing the specific metrics (total users, new users, sessions, etc.). This distinct resource and action set it apart from sibling tools like ga4_top_pages or ga4_traffic_sources, and it is immediately evident what the tool accomplishes.

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 implies its use for obtaining a high-level user metrics summary, but it does not explicitly state when to choose this over alternatives such as ga4_run_report for custom reports. There is no exclusionary guidance or mention of conditions where this tool is inappropriate, leaving the usage context implicit.

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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