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

Annotations already declare this as a safe, read-only, idempotent, closed-world tool. The description adds substantial behavioral context beyond that: the two-step catalog/target workflow, return fields such as share of shelf and first-position win rate, product omission counts, and the meaning of the no_product_catalog error.

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 front-loaded with purpose and then explains the two call modes and error case. It is dense but every sentence contributes useful information; it could be slightly more scannable, but there is no wasted filler.

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?

There is no output schema, so the description carries the burden of explaining return values. It does so thoroughly for both the no-target catalog call and the targeted call, including metrics, holders, measurement timing, and an error semantic, making it complete for correct invocation.

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 100%, so the schema already documents all three parameters. The description still adds useful semantics by explaining that target_id should come from the catalog returned without a target and that target_kind/target_id must be passed together, though it does not add much beyond the schema for project_id.

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 states a specific verb and resource: reading AI answer shelf recommendations for products and merchants in an ecommerce project. It clearly separates this from related visibility tools by naming read_visibility_overview for brand mentions, so an agent can distinguish it from siblings.

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?

It gives explicit sequencing: call first with no target to get the catalog, then call again with target_kind and target_id for a category or product. It also states the alternative for non-ecommerce brands when no_product_catalog is unavailable, which is exactly the when-not guidance an agent needs.

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