Recommend a component

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

No annotations are provided, so the description carries full burden. It discloses that output includes whenNot warnings for misuse avoidance, which adds behavioral context. However, it lacks information on authorization needs, whether it mutates data, or any rate limits.

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, front-loaded sentence with no wasted words. Every part adds value: action, input, output, and benefit.

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 no output schema and a limited description, the tool could benefit from more details about the output format (e.g., structure of recommendations) and explicit parameter coverage. The tool has many siblings with similar names; more context would help differentiate.

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 0%, and the tool description only alludes to the useCase parameter ('Describe a use case in natural language'). The limit parameter is not mentioned, and no additional semantics are provided for how to use or format parameters.

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: it takes a natural language use case and returns ranked component recommendations with whenNot warnings. It distinguishes from siblings like list_components (just lists) and search_components (query-based search).

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 use context (describing a use case to get recommendations) but does not explicitly state when not to use it or contrast with alternative tools like search_components or list_components. The mention of 'so the agent avoids misuse' implies guidance but is not explicit.

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