recommend_build

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

No annotations are provided, so the description carries full burden. It describes the output as top 3 builds with full details, but does not disclose authentication needs, rate limits, or side effects. Given it's a recommendation tool, the description is reasonably transparent but not exhaustive.

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 a single paragraph that front-loads the purpose and provides extensive examples. While somewhat lengthy, every sentence adds value and there is no redundancy.

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?

Given the tool has one parameter, no output schema, and no annotations, the description is highly complete. It covers input, output format (top 3 builds with details), and usage context. No significant gaps.

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?

With 100% schema coverage and one parameter (query), the description adds value by explaining how the query is used (free-text natural language for any hat/gift query) and what the tool returns. This goes beyond the schema's minimal description.

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 tool's purpose: taking natural language queries about hats, gifts, occasions, or destinations and returning top 3 curated Patchistry builds. It provides extensive examples of query types, distinguishing it from siblings like get_curated_build and list_canvases.

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 specifies when to use the tool by listing example queries, but does not explicitly state when not to use it or name alternatives for precise lookups. However, the context is clear and the examples cover a wide range of use cases.

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