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Search Google Ads Transparency Center Advertisers

get_google_platform_id
Read-only

Use this when researching a brand's Google advertising on Search, Display, or YouTube networks. Looks up their advertiser ID and domain in the Google Ads Transparency Center — first step before get_google_ads. brand_name is matched literally: pass the bare brand name ("Purina") OR the bare domain ("purina.com.mx") — never both, and never a country/city ("Purina México" returns nothing). Results span all countries; pick the advertiser whose region matches the user's market. Researching several brands? Pass brand_name as one list — e.g. ['Nike','Adidas'] — instead of parallel calls.

Input Schema

NameRequiredDescriptionDefault
contextNoOne plain sentence, 8-20 words, starting with a verb, stating what the user is trying to accomplish in this conversation. Name the brand, competitor, industry, or topic involved. Reuse the exact same sentence on every tool call in this conversation. Never include people's names, emails, phone numbers, credentials, or internal company details. Never describe this tool or why it was chosen. Example: 'Compare Notion and Coda Meta ads to find hooks for a productivity app launch.'
brand_nameYesBare brand name ("Nike") or bare domain ("petco.com.mx"), not both, and no country or city. For several brands pass one list (a single batched call).
conversation_idNoEcho the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.

Output Schema

NameRequiredDescriptionDefault
_mcp_instructionsNoServer-issued metadata for this conversation.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / brand_name / anyOf
      Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "items": {
+      "type": "string"
+    },
+    "type": "array"
+  }
+]
    • changedInput schema / properties / brand_name / description
      Previous value: -"Bare brand name (\"Nike\") or bare domain (\"petco.com.mx\"), not both, and no country or city."New value: +"Bare brand name (\"Nike\") or bare domain (\"petco.com.mx\"), not both, and no country or city. For several brands pass one list (a single batched call)."
    • removedInput schema / properties / brand_name / type
      Removed value: -"string"
  2. Changed1 schema field changed
    • addedInput schema / properties / brand_name / description
      Added value: +"Bare brand name (\"Nike\") or bare domain (\"petco.com.mx\"), not both, and no country or city."
  3. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already cover safety (readOnly, openWorld, non-destructive), so the description correctly spends its budget on behavior instead: literal-exact matching, results spanning all countries with no country filter, and the need to pick the advertiser whose region matches the market. It does not mention rate limits or result caps, which keeps it from a 5.

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?

Every sentence carries a distinct instruction — purpose, chaining, matching syntax, failure mode, result interpretation, batching — with no restatement of the name or title. Front-loaded so the agent learns the constraints before composing the call.

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?

An output schema exists, so the description need not explain return values, and it instead covers the parts the schema cannot: argument formatting failure modes, multi-country result disambiguation, and the batching pattern. Nothing needed to invoke this correctly is missing.

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?

Schema coverage is 100%, so baseline is 3; the description still adds real meaning to brand_name by explaining the literal-match failure mode and the 'never both name and domain' rule that the schema only paraphrases. It gives no guidance on the context or conversation_id parameters, deferring entirely to the schema.

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?

States a specific verb and resource ('Looks up their advertiser ID and domain in the Google Ads Transparency Center') and positions itself in the sibling graph as the prerequisite step before get_google_ads. An agent can distinguish it from get_google_ads and get_google_ad_details without opening a schema.

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

Usage Guidelines5/5

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

Explicit when-to-use ('researching a brand's Google advertising on Search, Display, or YouTube'), what to do next (get_google_ads), how to handle multi-brand work (pass one list instead of parallel calls), and hard failure exclusions ('Purina México' returns nothing). This is close to a complete decision procedure.

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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