Search design systems

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It explains the return type (compact metadata with a namespaced id), the meaning of fetchable:false results (real but gated), and mentions the response's gating map for further detail. It could also cover rate limits or authentication, but for a search operation this is reasonably transparent.

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 long and front-loaded with the core search behavior and examples. It then covers return format, gating semantics, and use case without any filler. Every sentence earns its place, and the structure flows naturally from what it does to how results behave to when to use it.

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 6 parameters and no output schema, the description covers the essential context: scope of search, query types, return format, gating behavior, and the intended use case. It does not fully describe the output structure or pagination, but the gating map mention gives agents enough to navigate results safely. This is complete enough for successful invocation in most workflows.

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 does not add much parameter-level detail beyond the schema, but the schema already provides rich guidance, especially for the 'deep' parameter. The tool description's examples ('stripe', 'dark fintech dashboard') reinforce query semantics without replacing schema documentation.

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 the specific verb 'Search' with a clear resource ('every enabled catalog at once for a design system') and clarifies the query types: brand, style, or mood, with concrete examples. It also notes the output shape (compact metadata with namespaced id), which helps distinguish this from sibling tools like get_design or get_design_sections.

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 states when to use this tool: 'when starting new UI work and the project has no DESIGN.md yet.' This is clear context, though it does not explicitly mention alternatives or when NOT to use it, relying on sibling tool names to cover that gap.

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