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AdsAgent — Google Ads MCP

google_ads_recommendation_apply_prepare

Prepare applying one Google Ads recommendation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
customer_idYes10-digit Google Ads customer id.
connection_contextNo
recommendation_resource_nameYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / connection_context
      Added value: +{
      +  "pattern": "^mctx_v1_[0-9a-f]{64}$",
      +  "type": "string"
      +}
  2. First observed

TDQS

C2.4/5.0
Behavior2/5

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

Annotations tell the agent this is not read-only, not destructive, and not open-world, so the basic safety envelope is covered. The description adds nothing beyond that: it does not say whether 'prepare' mutates anything, produces a staged payload, expires, or requires a confirmation step — the key behavioural trait for a prepare/confirm tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single short sentence that is front-loaded and free of padding. It earns no penalty for bloat, but it is under-specified rather than concise, so it sits at the minimum-viable level for this dimension.

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?

Given a prepare/confirm sibling pair, 3 parameters at 33% schema coverage, and no output schema, the description is too thin. An agent cannot tell from it what the prepare call returns, whether it is idempotent, or that a confirm call is required to actually apply the recommendation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 33%: customer_id is documented but recommendation_resource_name and connection_context are not. The description does not compensate — it never states the expected resource-name format (e.g., customers/{id}/recommendations/{id}) or that connection_context is a context token, which is exactly the gap the description should fill.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a verb phrase plus resource ('Prepare applying one Google Ads recommendation'), so the nominal purpose is legible. However, it does not distinguish this tool from its sibling google_ads_recommendation_apply_confirm, nor explain what the 'prepare' phase actually produces, leaving the two-phase intent implicit.

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 when-to-use guidance at all: no statement that this is the first half of a prepare/confirm pair, no mention that google_ads_recommendation_apply_confirm must follow, and no contrast with google_ads_recommendation_dismiss_prepare. The agent must infer the workflow from the naming convention alone.

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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