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

Annotations already indicate readOnlyHint=true and destructiveHint=false, but the description adds meaningful behavioral context: country adjectives are parsed server-side, similarity seeds must resolve to an active catalog brand, unknown/ambiguous seeds return no brands, and shipping-origin signals are best-effort and separate from product availability. These details go well beyond the annotations and help prevent false expectations.

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 four sentences, front-loaded with the core purpose, and every sentence adds useful information: examples, an alternative, server-side parsing behavior, and a caveat about best-effort signals. There is no redundancy or filler, striking an ideal balance between detail and brevity.

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 an 8-parameter tool with no required parameters and an output schema, the description covers selection criteria, invocation modes, parameter interplay, and edge cases (unknown seeds, optional queries). The output schema handles return-value documentation, so nothing essential is missing. It is complete for an agent to select and invoke the tool correctly.

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 baseline is 3. The description adds value by clarifying the relationship between query and structured filters, noting query optionality, and explaining the best-effort nature of shipping filters. While much of this repeats schema descriptions, the description consolidates it into a cohesive usage narrative, earning slightly above baseline.

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 opens with a specific verb+resource statement: 'Find fashion brands using natural language, structured filters, or both.' It clearly differentiates from siblings by explicitly directing plain 'brands like X' queries to find_similar_brands, and the brand-vs-product distinction is obvious from the sibling names. This is unambiguous and useful.

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 explicit when-to-use guidance with rich examples ('Italian streetwear brands', 'Scandinavian minimalist brands') and names an alternative: 'For a plain "brands like X" request, use find_similar_brands.' It also clarifies when query is optional and how to combine it with structured filters, giving the agent clear decision criteria.

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