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JavaScript
Remote

Search Google ads

search_google_ads
Read-only

Retrieve Google Ads for a known advertiser by domain or ID, returning compact JSON with ad format, image, first and last shown dates.

Instructions

Structured Google Ads Transparency pull for ONE advertiser (by domain or advertiserId) — use when you know the brand; use research_ads for open-ended research. Deliberately fetches the cheap BASIC listing (get_ad_details=false, ~1 credit — the detailed variant with per-ad headlines costs 25 credits/call and is not exposed here). Returns compact JSON {advertiser, format, adUrl, image, firstShown, lastShown} per ad.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNomax ads returned (1–25, default 8)
domainNothe advertiser's domain, e.g. nike.com
regionNo2-letter region, default US
advertiserIdNoGoogle advertiser id (AR…) when the domain is ambiguous

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.161
    • changedOutput schema / (root)
      Previous value: -{
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "additionalProperties": false,
-  "properties": {
-    "ads": {
-      "description": "the compact ad objects ({advertiser, format, adUrl, image, firstShown, lastShown})",
-      "items": {},
-      "type": "array"
-    },
-    "found": {
-      "description": "total ads found upstream",
-      "type": "number"
-    }
-  },
-  "type": "object"
-}New value: +null
  2. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark this readOnlyHint=true and destructiveHint=false, but the description adds valuable behavioral context: it deliberately fetches the BASIC listing, sets get_ad_details=false, costs ~1 credit, and explicitly states that the 25-credit detailed variant is not exposed here. The return format is also summarized, going well beyond the annotations.

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?

Three dense, purposeful sentences with zero filler. The core scoping rule is front-loaded first, the alternative is named second, and the cost/return details are packed efficiently. Every sentence earns its place.

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?

Despite having no output schema, the description summarizes the return shape, cost behavior, and lookup modes. Combined with fully described parameters and safety annotations, an agent has everything needed to invoke this tool correctly without guessing.

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 schema already documents all four parameters with useful descriptions. The description adds clarity that the tool targets ONE advertiser and that domain or advertiserId are the primary lookup keys, but this is reinforcing schema semantics rather than adding substantial new meaning.

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 states a specific verb and resource: a structured Google Ads Transparency pull for ONE advertiser, keyed by domain or advertiserId. It distinguishes itself from the sibling research_ads by explicitly contrasting targeted lookups with open-ended research, so an agent can tell them apart immediately.

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?

It gives an explicit when-to-use rule ('use when you know the brand') and names the alternative ('use research_ads for open-ended research'). It also discloses the cheap BASIC variant versus the expensive detailed variant, telling the agent exactly what behavior to expect and why this tool is the economical choice.

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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