recommend_component

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states the output format ('structured verdict, not a list') and mentions reliance on Mobbin references for custom builds. It does not detail failure modes or edge cases, but for a non-destructive decision tool, this is sufficient and adds meaningful context beyond the raw schema.

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 three sentences, each earning its place: the first states the core purpose, the second clarifies the output type, and the third lists usage triggers. It is front-loaded with the primary action and avoids any fluff or redundant details.

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 tool with no output schema, the description adequately explains the return value (a verdict with two possibilities) and the context in which it should be used. It also notes it is not a search tool, which is critical for correct invocation. Combined with full schema coverage for parameters, nothing essential is missing.

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 description coverage is 100%, so the baseline is 3. The description itself adds no additional parameter-specific semantics; the schema already explains each parameter clearly (e.g., 'component_need' should be specific, not a category). The description does not repeat or enhance this, so it stays at the 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 uses a specific verb ('judges') and resource ('UI component need'), clearly states the decision outcome (use_existing | custom_build), and explicitly distinguishes itself from search tools by saying 'not a list of search results'. This makes the tool's purpose unambiguous even without siblings.

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?

It provides explicit trigger conditions: 'whenever you are about to scaffold a new, non-trivial UI component from scratch', 'when you're unsure your own default output will look production-quality', or 'when the user references a specific app's pattern'. This is direct, actionable guidance on when to invoke the tool.

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