Google Ads MCP Server

apply_recommendation

Apply a Google Ads recommendation to a customer account using the customer ID and recommendation ID, executing the suggested change directly.

Instructions

Apply a Google recommendation

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "customerId": "<customerId>",
      "recommendationId": "<recommendationId>"
    },
    "name": "apply_recommendation"
  }
}

Input Schema

Name	Required	Description	Default
`customerId`	Yes	Google Ads customer ID
`recommendationId`	Yes	Recommendation ID

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 9, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description is the only source of behavioral disclosure. It states that the tool 'applies' a recommendation, implying a mutating action, but it does not disclose potential irreversibility, permission requirements, or the impact on the account. This is a significant gap for a modification tool.

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?

The description is a single, concise sentence that is front-loaded with the action and resource. There is no redundant information or unnecessary detail, making it highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a mutating tool with no annotations and no output schema, yet the description only states the basic action. It does not explain what happens after applying, whether it is reversible, or any prerequisites. The simple schema does not compensate for this lack of operational context.

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?

The input schema provides descriptions for both parameters (customerId and recommendationId) with 100% coverage. The description adds no additional parameter-specific context, so a baseline score of 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?

The description 'Apply a Google recommendation' clearly identifies the action (apply) and resource (recommendation), and it is distinct from sibling tools like get_recommendations and dismiss_recommendation. However, it lacks detail on what 'applying' entails or the specific scope of the recommendation.

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?

No guidance is provided on when to use this tool versus alternatives such as get_recommendations or dismiss_recommendation. There is no mention of prerequisites, typical workflow, or situations where applying is inappropriate.

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

Deploy Server