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AdsAgent — Google Ads MCP

google_ads_mutation_receipt_get

Destructive

Get a tenant/customer-bound lifecycle receipt for one native Google mutation draft. May update private draft expiry, payload-mismatch or uncertain status. This never retries or changes Google Ads.

Input Schema

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

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

B3.4/5.0
Behavior4/5

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

Annotations declare destructiveHint=true and readOnlyHint=false, which is surprising for a 'get' tool, and the description explains the mutation: it may update private draft expiry, payload-mismatch, or uncertain status. It clarifies it doesn't touch Google Ads or retry. This adds meaningful context beyond annotations, though it could say more about what 'update' entails.

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?

Two compact sentences that front-load the core action and its side effects. Every clause carries information, though 'tenant/customer-bound lifecycle receipt' is dense.

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

Completeness3/5

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

For a tool with no output schema and sparse parameter documentation, the description misses details about the receipt format and the connection_context parameter. It covers the mutation behavior well but leaves gaps an agent would need to call it confidently.

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 coverage is only 33%; the customer_id is documented in the schema, but draft_id and connection_context are not described. The description gives no meaning for draft_id or connection_context, so it does little to compensate for the low coverage.

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: get a lifecycle receipt for one native Google mutation draft, scoped to a tenant/customer. It's clearly distinct from sibling confirm/prepare tools, though the concept of a 'lifecycle receipt' is somewhat jargon-heavy.

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 description implies usage (check the receipt of a draft) but doesn't explicitly say when to use this versus google_ads_task_get or google_ads_mutation_reconcile. It does clarify it never retries or changes Google Ads, which helps exclude misuse.

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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