get_component_suggestions

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

With no annotations, the description carries the full burden for behavioral disclosure. It only mentions 'AI-powered', but doesn't disclose whether it's read-only, side effects, rate limits, or response structure. This is insufficient for a tool that likely makes external AI calls.

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?

A single, front-loaded sentence that concisely states the tool's purpose without unnecessary words. Every word earns its place.

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?

The tool has no output schema and no annotations, so the description must explain return values and usage context. It does not specify what the suggestions look like, how many are returned, or the format for preferences. This is a significant gap for a tool that generates suggestions.

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 description maps 'requirements' to the required 'requirement' parameter, which is already documented in the schema. It adds no new information about 'context' or 'preferences'; schema descriptions cover those adequately. At 67% schema coverage, the description provides marginal additional value.

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: retrieving AI-powered component suggestions based on a requirement. The verb 'Get' and resource 'component suggestions' distinguish it from sibling tools like generate_ui_component or optimize_component, even without explicitly naming alternatives.

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?

No guidance is provided on when to choose this tool over alternatives like generate_ui_component or search_tamagui_docs. The description simply states the function without context, prerequisites, or exclusions.

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