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Google Ads MCP Server

Suggest Geo Targets

suggest_geo_targets
Read-only

Convert location names like 'Phoenix, Arizona' into Google Ads geo target constant IDs so you can add location targeting to campaigns.

Instructions

Resolves human-readable location names (e.g. 'Phoenix, Arizona', 'Gilbert, Arizona') to Google Ads geo target constant IDs, for use with add_location_targeting.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
country_codeNoTwo-letter country code to scope the lookup.US
location_namesYesLocation names to resolve.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.2

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds that the operation is a name-to-ID resolution, but says nothing about ambiguous or unresolved names, rate limits, or partial-match behavior, so it adds only modest context beyond annotations.

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?

A single tight sentence with the core transformation front-loaded and the downstream dependency appended. Every clause carries information, though the example pair is mildly redundant.

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 two-parameter lookup with an output schema present and read-only annotations, the description covers the essential purpose and integration point. Only edge-case behavior (no match, ambiguous match) is left unaddressed.

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?

Schema description coverage is 100%, so the baseline is 3. The examples ('Phoenix, Arizona') clarify the expected granularity of location_names slightly, but country_code scoping and array/format expectations are already documented in the schema.

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?

States a specific verb ('resolves') and a precise transformation (human-readable location names -> Google Ads geo target constant IDs), with concrete examples. It is clearly distinguishable in function from the sibling tools, though it does not name a sibling it is not.

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

Usage Guidelines4/5

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

Explicitly ties the tool to its downstream consumer: 'for use with add_location_targeting', which tells the agent where this fits in the workflow. It stops short of stating when not to use it or how it relates to other lookup tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.