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johnoconnor0

Google Ads MCP Server

by johnoconnor0

google_ads_upload_customer_match

Upload hashed customer data (emails, phones, addresses) to Google Ads for targeting across Search, YouTube, Gmail, and Display networks.

Instructions

Upload Customer Match data (emails, phones, addresses).

Customer Match allows you to use your customer data to reach them on Google Search, YouTube, Gmail, and Display Network. Data is hashed before upload for privacy.

You can either upload to an existing list (provide user_list_id) or create a new list (provide list_name).

Args: customer_id: Customer ID (without hyphens) user_list_id: Existing user list ID to upload to (optional) list_name: Name for new list (required if user_list_id not provided) emails: List of email addresses phones: List of phone numbers (E.164 format recommended: +12345678900) first_names: List of first names (must match emails/phones index) last_names: List of last names (must match emails/phones index) countries: List of country codes (e.g., "US", "UK") zip_codes: List of postal codes

Returns: Success message with upload job details

Example: google_ads_upload_customer_match( customer_id="1234567890", list_name="Email Newsletter Subscribers", emails=[ "customer1@example.com", "customer2@example.com", "customer3@example.com" ] )

Privacy Note: All data is automatically hashed with SHA256 before upload. Google cannot see the original data.

Match Rate: Typically 30-70% of uploaded records will match to Google users.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
customer_idYes
user_list_idNo
list_nameNo
emailsNo
phonesNo
first_namesNo
last_namesNo
countriesNo
zip_codesNo

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 hashing (SHA256), privacy, typical match rate (30-70%), and return type. However, it omits details on append vs overwrite behavior, rate limits, or error conditions.

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-structured with a summary, details, args list, returns, example, and notes. It is somewhat verbose but front-loads the key action. Minor redundancy, e.g., the privacy note could be integrated.

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 9 parameters and no annotations, the description covers purpose, parameters, behavior, and example. It mentions output schema briefly. Lacks error handling and limits, but is sufficient for most use cases.

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 coverage is 0%, so the description compensates fully. It provides detailed parameter descriptions including phone format (E.164), index matching for names, country codes, and an example. This significantly aids correct invocation.

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 'Upload Customer Match data (emails, phones, addresses)' and explains the purpose of Customer Match for reaching users on Google properties. It distinguishes from sibling upload tools by focusing specifically on Customer Match.

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 explains when to use existing vs new lists but does not compare with alternatives like upload_offline_conversions or upload_store_sales. It lacks explicit guidance on when not to use this tool.

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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