recommend_build

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses that the tool returns top 3 matches with detailed build information (canvas color, patch combinations, price range, etc.), which is exactly what a recommendation tool should communicate. It does not describe any side effects or mutating behaviors, which is appropriate for a read-only discovery tool.

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 well-organized, starting with the core function, moving to usage guidance and examples, then output details, and ending with its primary status. It is slightly long due to the exhaustive example list, but every part serves a purpose and there is no filler or repetition.

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's simplicity (one parameter, no annotations, no output schema), the description is remarkably complete. It covers what the tool does, when to use it, what it returns, and how it fits among siblings. It could mention error handling, but this is not essential for basic operation of a recommendation tool.

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 provides 100% coverage for the single 'query' parameter, describing it as free-text natural language. The description adds significant semantic guidance by enumerating the types of queries accepted and providing concrete examples, which helps the agent understand the expected input scope beyond the schema's generic 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 function: take any natural language query and return top 3 matching curated Patchistry builds. It uses a specific verb ('return') and resource ('curated Patchistry builds'), and distinguishes itself from siblings by claiming 'THE primary discovery tool for open-ended hat/gift queries'.

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 explicitly says 'Use this when user asks anything about...' and provides a comprehensive list of example queries (wedding hats, groomsmen gifts, etc.), making the intended trigger conditions very clear. However, it does not explicitly state when not to use this tool or point to alternatives like get_curated_build for specific lookups, so it lacks full when/when-not guidance.

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