Generate a portable DESIGN.md for the authenticated tenant — brand color, type, tokens, and component inventory — that any AI agent can read to generate on-brand UI. Pass format="markdown" (default) for the DESIGN.md string or format="json" for structured data. Output is deterministic for a given tenant.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Output format. "markdown" returns the DESIGN.md string; "json" returns structured data. | markdown |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden, and it adds useful behavioral traits: output is deterministic for a given tenant, and the format can be markdown or JSON. It discloses the 'portable' nature and the fact that it requires an authenticated tenant. It doesn't cover error cases or rate limits, but for a simple read-like generator, this is sufficient.
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 defines purpose and content, the second explains usage, and the third adds the determinism behavior. It is front-loaded and has zero fluff.
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 one optional parameter, no output schema, and no annotations, the description is complete. It covers what it returns, content included, format options, the authentication context, and determinism. An agent has enough to select and invoke it correctly without further clarification.
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 input schema already documents the 'format' parameter with enum, default, and description, so schema coverage is 100%. The description merely restates what the schema says ('Pass format="markdown" ... or format="json"'), adding no additional meaning or guidance on selection. Baseline 3 is appropriate.
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 action ('Generate a portable DESIGN.md') and the resource/scope ('for the authenticated tenant'), with a specific content list (brand color, type, tokens, component inventory). This distinguishes it from sibling tools like get_design_guidelines or get_tenant_theme, which focus on narrower aspects.
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 context—this produces a DESIGN.md that any AI agent can read to generate on-brand UI—implying when it should be used. However, it does not explicitly mention when not to use it or compare it to alternatives like get_tenant_theme or get_design_guidelines, so it misses the full 'when-not' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools have distinct purposes, but there is some overlap between review_generated_code and validate_component_usage, and between list_components and search_components. However, descriptions clearly differentiate their intended use cases, reducing ambiguity.
All tools follow a consistent verb_noun pattern with snake_case (e.g., get_component_info, generate_component, review_generated_code). The naming is predictable and clearly indicates the action.
14 tools is well-scoped for a UI design system assistant. It covers component discovery, detailed info, design guidelines, theme, code generation and validation, and prototype management without being overwhelming.
The tool set covers the full workflow: discover components, get patterns, get design guidelines, generate and validate code, get tenant theme, deploy prototypes and collect feedback. No obvious gaps for the intended usage scenario.