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Glama

Ads recommendations apply

ads.recommendations.apply
Destructive

Apply Google Ads recommendations using resource names returned by ads.recommendations.list, with optional confirmation for guarded writes.

Instructions

Apply Google Ads recommendations by resource name (from ads.recommendations.list).

Input Schema

NameRequiredDescriptionDefault
confirmNo
customer_idYes
resource_namesYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.10.0

TDQS

C2.9/5.0
Behavior2/5

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

Annotations already declare destructiveHint=true, idempotentHint=false and openWorldHint=true, so the agent knows this mutates. The description adds nothing beyond that — no statement that applied changes are irreversible, no rate/consent behavior, and notably no mention of the 'confirm' parameter which appears to be a safety gate on a destructive operation.

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?

One short sentence, front-loaded with verb and resource, and the parenthetical pointer is genuinely useful rather than filler. Nothing is redundant, though its brevity leaves important gaps that are counted under other dimensions.

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?

For a destructive, non-idempotent mutation with three undocumented parameters (including a confirm flag) and an output schema, a single sentence is insufficient. An agent has no way to learn from the description what confirm expects or what safeguards to exercise before applying.

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 0%, so the description must carry parameter meaning, but it only clarifies resource_names via the list-tool pointer. customer_id is undocumented and 'confirm' is entirely unexplained even though it likely gates a destructive apply — the highest-risk parameter gets zero semantics.

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 (Apply) and resource (Google Ads recommendations) plus the source of the identifying input ('by resource name (from ads.recommendations.list)'). This separates it from the sibling lister; it does not explicitly separate it from ads.recommendations.dismiss, but the verb is unambiguous.

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?

Implies the workflow (list first, then apply using returned resource names) which is decent implied usage guidance. However it never states prerequisites, when to prefer dismissal, or what happens if a recommendation is stale/already applied — no exclusions at all.

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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