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

The description adds meaningful behavioral context beyond the readOnlyHint and destructiveHint annotations: it reveals the vector-based mechanism, the requirement for active/merged aliases, the 'returns no brands' edge case for unknown/ambiguous inputs, and a hard limit of 20 results. This gives the agent a clear model of what will happen.

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 two dense sentences, front-loaded with the core purpose, and every word provides useful information without redundancy. It efficiently covers the input constraint, mechanism, edge case, and output limit.

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?

The combination of description, annotations, and output schema fully covers the tool's behavior. The description handles the key business logic (seed requirements, no results, cap), while the output schema presumably documents return structure, so no further explanation is needed.

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 schema already describes 'brand' and 'country' explicitly, and the description adds extra semantics for 'brand' (must be active or merged alias) and 'limit' (implied by 'Returns up to 20'). The description does not discuss 'country' beyond the schema, but the schema already covers it, so the marginal value is still positive.

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 the verb 'find' and the resource 'similar brands', and specifies the method ('using brand-profile vectors generated during product indexing'). It also distinguishes from siblings like discover_brands and find_similar_products by focusing on brand similarity based on a seed brand.

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 provides clear context: the seed must be an 'active catalog brand name or merged alias', and unknown seeds return nothing. However, it does not explicitly name alternative tools or state when not to use this tool, such as 'for browsing all brands use discover_brands'.

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