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johnoconnor0

Google Ads MCP Server

by johnoconnor0

google_ads_store_visits

Retrieve store visit conversion data from Local campaigns to measure how ad interactions lead to physical store visits. Filter by campaign and date range.

Instructions

Get store visit conversion data for Local campaigns.

Retrieves detailed store visit conversion metrics. Store visits are tracked when users who saw or clicked an ad subsequently visit a physical location.

Args: customer_id: Google Ads customer ID (10 digits, no hyphens) campaign_id: Optional campaign ID to filter (returns all if not specified) date_range: Date range - LAST_7_DAYS, LAST_30_DAYS, LAST_90_DAYS, etc.

Returns: Dictionary with store visit data including: - campaigns: List of campaigns with store visit conversions - total_store_visits: Total store visits across all campaigns - total_value: Total value of store visits - has_data: Whether any store visit data is available

Example: Get store visit conversions for all local campaigns: google_ads_store_visits( customer_id="1234567890", date_range="LAST_30_DAYS" )

Important Notes: - Requires Google My Business integration - Store visit data takes 4-6 weeks to accumulate - Uses probabilistic modeling based on location services - Aggregated and anonymized data for privacy

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
customer_idYes
campaign_idNo
date_rangeNoLAST_30_DAYS

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 key behaviors: requires Google My Business integration, 4-6 week data accumulation, probabilistic modeling, and aggregation for privacy. This is sufficient context for an agent to understand the tool's constraints.

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 well-organized with clear sections (summary, args, returns, example, important notes). It is concise, front-loads the purpose, and each sentence adds value without redundancy.

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 that the tool has an output schema (documenting return fields) and 3 parameters, the description covers all necessary context: parameter details, return structure, data latency, and prerequisites. No significant gaps are present.

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

Parameters4/5

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

Input schema has zero description coverage, so the description adds essential meaning: customer_id format (10 digits, no hyphens), campaign_id optional with filtering behavior, and date_range with explicit examples (LAST_7_DAYS, LAST_30_DAYS). This is thorough for the three parameters.

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 retrieves store visit conversion data specifically for Local campaigns. The verb 'Get' and resource 'store visit conversion data' are precise, and the tool is well-distinguished from sibling reporting tools like google_ads_local_performance.

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 usage for local campaigns and provides important context (GMB integration, data latency) but does not explicitly state when to use this tool over alternatives or when not to use it. No exclusion criteria or sibling comparisons are offered.

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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