get_listing_groups

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

With no annotations, the description carries the burden of behavioral disclosure. It includes a notable behavior: 'Empty trees are valid results,' which sets expectations about possible empty responses. However, it doesn't disclose other important behaviors such as whether the campaign must be a Shopping campaign, how deep the tree goes, or any pagination or latency implications.

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 (one sentence plus a note) and front-loads the key concept ('Listing-group trees'). The note about empty trees adds value without padding. It could be slightly more structured, but it is efficient and to the point.

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's moderate complexity (tree structure with parent links, dimensions, exclusions) and the lack of output schema, the description gives a good high-level overview but omits details like the exact data shape, whether the tree is nested or flat, and how to interpret the response. The note about empty trees is helpful. There is room for improvement in specifying the response format.

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?

Schema coverage is 0% because the property titles ('Campaign Id' and 'Customer Id') are generic and the schema does not provide descriptions. The description does not elaborate on the parameters beyond the tool's general scope. For instance, it does not explain that campaign_id is required and identifies the target campaign, or that customer_id is optional. This is a gap for agents to understand required vs. optional and how to fill them.

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 identifies a clear verb ('get') and resource ('listing-group trees') and specifies the scope ('per PMax asset group and standard Shopping ad group for the requested campaign'). It conveys the purpose well, though it does not explicitly distinguish from sibling tools like get_shopping_performance or get_product_status, which are thematically related.

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 usage in the context of campaigns and ad groups, suggesting it is used for retrieving listing-group structure rather than performance data. However, it lacks explicit guidance on when to choose this tool over alternatives or any exclusions (e.g., 'use get_shopping_performance for metrics'). No when/not-when guidance is provided.

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