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

With no annotations provided, the description carries the full burden of disclosure. It goes beyond a simple action statement by revealing the model's backing (522 learned pairs, 95% agreement) and, crucially, its failure behavior: returning null rather than guessing when the phrase names no category. This is strong, decision-relevant transparency.

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?

Three sentences, all earning their place: the primary action with examples, the underlying reliability evidence, and the null-return behavior. The most important information is front-loaded, and there is no filler or repetition of schema content.

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 single-parameter tool with no output schema, the description is complete: it states what input is expected, shows valid examples, explains how the mapping works, and explicitly covers the null case. An agent has everything needed to call the tool correctly and interpret an unexpected result.

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% and the schema already describes 'term' as 'A product type, in Persian or English.' The description adds illustrative examples and clarifies that a 'phrase' is acceptable, but it does not substantially extend the schema's parameter meaning, so the baseline 3 is appropriate.

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 names a specific verb ('Map') and resource ('any Persian or English lighting phrase onto canonical taxonomy'), with concrete examples like «داون‌لایت توکار» and 'high bay'. This clearly distinguishes the tool from product/brand lookup siblings such as get_product or list_brands.

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 establishes clear usage context: use this when you have a lighting phrase, in Persian or English, that needs mapping onto a canonical category. It does not explicitly name alternatives or exclusions relative to sibling tools, but the domain and intent are unambiguous enough for an agent to select it appropriately.

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