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johnoconnor0

Google Ads MCP Server

by johnoconnor0

google_ads_upload_call_conversions

Upload phone call conversion data to Google Ads, specifying caller ID, call times, and optional value. Track conversions from inbound calls.

Instructions

Upload call conversion data for phone calls that converted.

Args: customer_id: Customer ID (without hyphens) conversion_action_id: Call conversion action ID call_conversions: List with: - caller_id: Phone number that called (E.164 format: +12345678900) - call_start_date_time: When call started - conversion_date_time: When call qualified as conversion - conversion_value: Optional conversion value - currency_code: Optional currency

Returns: Upload success message

Example: google_ads_upload_call_conversions( customer_id="1234567890", conversion_action_id="12345", call_conversions=[ { "caller_id": "+12025551234", "call_start_date_time": "2025-12-15 10:30:00-08:00", "conversion_date_time": "2025-12-15 10:35:00-08:00", "conversion_value": 500.00, "currency_code": "USD" } ] )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
customer_idYes
conversion_action_idYes
call_conversionsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations exist, so the description must fully disclose behavioral traits. It only states the upload action and parameter formats, but does not mention idempotency, duplicate handling, required permissions, error conditions, or side effects. This is insufficient for a mutation tool.

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 an Args section, Returns note, and an example. It is reasonably concise, though the example is lengthy but provides necessary clarification for the nested structure.

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?

The description adequately explains input parameters and return value, but lacks behavioral details (e.g., idempotency, error handling) and usage context. For a tool with no annotations and a complex nested parameter, it is functional but not comprehensive.

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?

The input schema has 0% description coverage, leaving the description to explain parameters. It adds crucial semantics for call_conversions, including field names, format hints (E.164, datetime), optional fields, and an example. It does not detail customer_id or conversion_action_id, but their purpose is clear from context.

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 clearly states the action ('Upload call conversion data') and the resource ('phone calls that converted'). It distinguishes from sibling upload tools by specifying 'call conversions' as opposed to offline conversions, customer match, etc., though it does not explicitly contrast with them.

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?

No guidance is provided on when to use this tool versus alternatives (e.g., google_ads_upload_offline_conversions). There are no prerequisites, exclusions, or contextual hints about typical use cases.

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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