get_product

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

With no annotations, the description must carry behavioral disclosure on its own. It indicates a read-style 'Get' operation and that the result is a full spec row, which is useful, but it does not address return shape, missing-model behavior, or any access constraints.

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 definition is a single front-loaded sentence with no filler; every word contributes. It is appropriately short for a one-parameter getter.

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?

For a simple getter, the one-liner is close to sufficient, but the absent output schema and lack of behavior/error details leave some ambiguity about what 'full spec' contains. It also lacks routing guidance to distinguish it from lookup_model and search_products.

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% and the only parameter, model_name, is documented only by its title. The description adds the context that the parameter refers to a 'lens model', but provides no format, exact-name requirements, or examples.

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 clear action ('Get') and target ('full spec row(s) for a lens model'), making it understandable as a retrieval operation for lens-model products. It does not explicitly contrast itself with siblings like lookup_model or search_products, so it stops short of full differentiation.

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 when-to-use guidance or alternatives are provided; the description only states what the tool does. An agent must infer that get_product is for exact model-name spec retrieval and that search_products or lookup_model should be used otherwise.

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