google_ads_recommendations

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

The description clearly discloses that it returns suggestions only and does not apply them, reinforcing the readOnlyHint annotation. It also lists the recommendation types and warns that new types are added over time, adding context beyond the annotation. It does not mention rate limits or authentication, but given the annotation covers safety, this is adequate.

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 structured with a leading purpose statement, a list of types, parameter details, and output format. It is somewhat long due to the enumerated types, but each section adds necessary information and the purpose is front-loaded. No filler or redundant phrasing is present.

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 is thorough for a read-only recommendations tool. It covers the action, scope, parameter semantics, and output structure (even though an output schema exists, the description adds useful detail). It also addresses the dynamic nature of recommendation types, making it complete for an agent to call correctly.

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?

With 0% schema description coverage, the description fully compensates by detailing each parameter: customer_id as a 10-digit account ID, types as an optional case-sensitive filter, dismissed with a default, and limit with a default. It also explains the output structure, making parameter semantics very clear.

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 a specific verb ('Read') and resource ('Google's optimization recommendations for a customer account'), and further enumerates the recommendation types, making its purpose unambiguous. It stands apart from siblings like google_ads_query or google_ads_list_resources by focusing solely on recommendations.

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 makes it obvious that this tool is for reading optimization recommendations and explicitly notes it does not apply them, which is useful. However, it does not explicitly contrast with alternative tools (e.g., google_ads_query for arbitrary data, google_ads_keyword_ideas for keyword ideas), leaving the when-not-to-use guidance implicit rather than explicit.

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