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AdLoop: Google Ads, Reddit Ads & GA4

by kLOsk
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Python
Remote

Google Ads recommendations

get_recommendations
Read-only

Retrieve Google Ads recommendations with projected metrics and estimated impact, flagging budget-increase suggestions and high-impact changes so you can prioritize optimizations.

Instructions

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

Returns each recommendation's 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.

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

Input Schema

TableJSON 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

TableJSON 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

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so safety is covered. The description adds genuine behavioral context beyond them: the caveat that budget-increase recommendations derive their projected gain from additional spend, and that high-impact suggestions are flagged, which helps an agent interpret results correctly.

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 operation, then a compact enumeration of returned fields. It spends several sentences on return-value detail that an existing output schema already conveys, which is mild redundancy, but the prose is tight and free of filler.

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 a full output schema and rich annotations, the description is complete enough for correct invocation: it names the resource, the optional scoping filters, and the interpretation caveat. The only gap is the absence of routing guidance against sibling reporting tools.

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% and all three parameters (campaign_id, customer_id, recommendation_types) are well documented inline, so the schema carries the load. The description adds no syntax, default, or filtering semantics beyond what the schema states; baseline 3 is appropriate.

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 and resource ('Retrieve Google's auto-generated recommendations with estimated impact'), making the operation clear. However, it never differentiates itself from reporting-oriented siblings such as get_campaign_performance or get_ad_performance, which an agent might reasonably consider an alternative.

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?

The description offers no explicit when-to-use guidance and no exclusions relative to the many sibling tools. Nothing tells the agent whether this is preferred over run_gaql or the performance getters when seeking optimization ideas.

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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