google_ads_recommendations

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

Annotations are absent, so the description should fully disclose behavior. It only states it returns a list of recommendations, without mentioning pagination, rate limits, or read-only nature. This is insufficient for a mutation-free tool.

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 concise but includes an args section that largely repeats the schema. It is front-loaded with the main purpose but could be tighter.

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?

Given the tool has 4 parameters and no annotations, the description covers parameter semantics adequately and mentions return type. However, it lacks behavioral context and differentiation from siblings, making it minimally viable.

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 description adds meaningful details beyond the schema titles, such as customer_id format ('without hyphens'), example recommendation types, limit range (1-100), and response_format values ('markdown' or 'json'). This compensates for the 0% schema coverage.

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 it retrieves recommendations using 'Get AI-powered optimization recommendations', but it does not differentiate from the sibling tool 'google_ads_get_recommendations' which likely has similar functionality.

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?

No guidance is provided on when to use this tool versus alternatives like google_ads_get_recommendations, apply_recommendation, or others. The description does not mention when not to use it.

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