Retrieve Google Ads recommendations with base-versus-potential cost and conversion projections to identify optimization opportunities before deciding on next actions.
Google's active recommendations with typed impact projections (base vs potential cost and conversions). Read-only; applying or dismissing routes through the guardrail plan flow.
| Name | Required | Description | Default |
|---|---|---|---|
| customer_id | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It discloses the operation is read-only, which is a key behavioral trait, and clarifies that apply/dismiss actions are not performed here but routed elsewhere. This adds meaningful context beyond the bare minimum, though it doesn't mention authentication, rate limits, or side effects (none expected for read-only).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler. The primary purpose is front-loaded, and the behavioral note about read-only and routing is concise. Every sentence contributes meaning, making it appropriately sized and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description conveys the return type (typed impact projections with costs and conversions) and the read-only nature. However, it omits details on the customer_id parameter and any filtering or pagination behavior. For a simple list tool with no output schema, this is mostly complete but could be improved with a note on the parameter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one optional parameter (customer_id) with 0% description coverage in the schema. The description does not mention or explain this parameter at all, so it fails to compensate for the missing schema documentation. The agent is left to guess what customer_id does, making this a significant gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists Google's active recommendations and specifies the type of data returned (base vs potential cost and conversions). It uses a specific verb 'list' and a resource 'recommendations', which distinguishes it from the many get_* siblings, though it doesn't name an alternative explicitly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for viewing recommendations and notes it is read-only, but doesn't explicitly state when to choose this over alternatives. It mentions that applying/dismissing goes through a different flow, which gives some context, but no specific alternative tool is named. It is adequate but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.