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AdLoop: Google Ads, Reddit Ads & GA4

AdLoop: Google Ads, Reddit Ads & GA4

by kLOsk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Google Ads recommendations

get_recommendations
Read-only

Retrieve Google Ads auto-generated recommendations with projected impact, current and potential metrics, and IDs to identify high-impact suggestions and flag budget-increase gains.

Instructions

Retrieve Google's auto-generated recommendations with estimated impact.

Returns each recommendation's ID, type, associated campaign/ad group, current (base) and projected (potential) metrics, and the estimated improvement. Includes insights that flag budget-increase recommendations (their projected gain comes from spending more) and highlight high-impact suggestions. The recommendation.id is what draft_dismiss_recommendations takes.

Google Ads API: https://developers.google.com/google-ads/api/docs/start

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_recommendations"
  }
}

Input Schema

NameRequiredDescriptionDefault
campaign_idNoOptional numeric campaign ID to scope to a single campaign.
customer_idNoGoogle Ads customer ID, digits with or without dashes (e.g. "123-456-7890"). Defaults to the configured ads.customer_id.
recommendation_typesNoOptional filter of Google Ads RecommendationType names, e.g. ["KEYWORD", "TARGET_CPA_OPT_IN", "MAXIMIZE_CONVERSIONS_OPT_IN", "RESPONSIVE_SEARCH_AD"]. Empty = all types.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.27.1
    • addedInput schema / properties / campaign_id / description
      Added value: +"Optional numeric campaign ID to scope to a single campaign."
    • addedInput schema / properties / customer_id / description
      Added value: +"Google Ads customer ID, digits with or without dashes\n(e.g. \"123-456-7890\"). Defaults to the configured ads.customer_id."
    • addedInput schema / properties / recommendation_types / description
      Added value: +"Optional filter of Google Ads RecommendationType\nnames, e.g. [\"KEYWORD\", \"TARGET_CPA_OPT_IN\",\n\"MAXIMIZE_CONVERSIONS_OPT_IN\", \"RESPONSIVE_SEARCH_AD\"].\nEmpty = all types."
  2. Addedv0.13.2
  3. Removedv0.13.0
  4. First observedv0.9.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety bar is low. The description goes beyond them by explaining what the return payload contains, that 'insights' flag budget-increase recommendations whose gain comes from extra spend, and that high-impact suggestions are highlighted — non-obvious interpretation guidance an agent would otherwise miss.

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?

Front-loaded with the core action and return shape, then the sibling cross-reference. Four short sentences plus a docs link; the trailing API URL is marginal but cheap. Slight padding in enumerating every returned field.

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?

With an output schema present and annotations covering read-only safety, the description need only set expectations — which it does by naming the insight flags and the recommendation.id hand-off. Nothing critical is missing for a zero-required-param read tool.

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 campaign_id, customer_id, and recommendation_types are fully documented in the schema. The description adds nothing about parameter behavior (e.g., how empty recommendation_types is handled) beyond the schema, so baseline 3 applies.

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 ('Retrieve Google's auto-generated recommendations') and immediately qualifies scope with 'estimated impact'. The reader can distinguish it from sibling read tools like get_campaign_performance without opening the schema.

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 cross-reference to draft_dismiss_recommendations ('The recommendation.id is what draft_dismiss_recommendations takes') gives a useful downstream routing cue, but there is no explicit statement of when to prefer this tool over alternatives (e.g., run_gaql or get_campaign_settings) or any exclusion. Usage is only implied.

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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