Mozaika

get_design_system

Get a product's full, LLM-verified design system so you can match its exact look. Use this for "design like " (e.g. site="Linear", "Stripe", "Figma").

Returns (default): color_scheme; colors with named roles (background, text, primary,
secondary, accent, link, button_bg, button_text); fonts + font_roles; type_scale;
spacing; primary/secondary button; framework + personality. All hex normalized.

Deep-decoded products additionally include measured button hover/focus states, a
shadow elevation scale (card/overlay/subtle), motion durations + easings, the
measured spacing scale, the brand's own CSS custom properties (css_vars), and
Icon DNA (icons: style outline/filled/duotone/3d, grid, stroke_weight, corner) —
all measured from the live page, not guessed. Match them exactly; pass the domain
to generate_asset(style_from=...) to strike icons in this exact style.

Your own private BYODS design systems (call list_my_design_systems) resolve first.

format: leave empty for the raw token dict. Pass "all" to also get paste-ready
DESIGN.md / Tailwind v4 / CSS variables / W3C tokens JSON, or a single format name
("tailwind", "css", "design_md", "tokens", "astryx") to get just that text.
"astryx" returns a ready Meta-Astryx defineTheme TypeScript file (measured
hover/press states + [light,dark] tuples baked in) — save it and run
`npx astryx theme build` for production CSS.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`site`	Yes
`format`	No

TDQS

A5/5.0

Behavior5/5

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

No annotations are present, but the description thoroughly discloses return payload structure, 'deep-decoded' extra data, measurement methodology, format options, and behavior across different inputs. It covers both default and optional outputs in detail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Though long, the description is information-dense and front-loaded with purpose. Each section adds value, from return details to format options, with no filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema or annotations, the description fully covers returns, variants, usage context, and parameter behavior, making it self-sufficient for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only defines site and format as strings, but the description explains site as a product domain (Linear, Stripe, Figma) and details every format option including 'all' and 'astryx', adding substantial meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves a product's full design system and explicitly ties it to 'design like <product>' use cases. It distinguishes itself from sibling tools like get_component or get_product by focusing on the complete design system.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly specifies when to use the tool (e.g., 'design like Linear/Stripe/Figma'), mentions BYODS resolution order, and references generate_asset for complementary icon creation. This provides clear context and alternatives.

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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TDQS

A3.9/5.0

Disambiguation3/5

The compare_* / get_* / search_* family creates real overlap: compare_components, get_component, get_recipe, and search_screens(kind="component") can all answer similar component questions, and the drift family (get_design_drift, get_design_history, list_design_changes) requires careful reading to pick the right one. However, the detailed descriptions mostly draw clear lines between cross-product comparison, single-spec retrieval, and corpus-level search.

Naming Consistency4/5

The server mostly follows a clean verb_noun convention: get_*, compare_*, list_*, search_*, validate_design, generate_asset. The pattern is highly consistent, though a few names differ slightly in style (audit_code vs validate_design vs get_score), and pluralization varies in tools like compare_components and compare_sections.

Tool Count3/5

At 23 tools this is on the heavy side, and several calls overlap in scope enough to feel redundant. That said, the server's broad purpose suggests a design system reference plus audit platform, so the count is justifiable; it could be consolidated into a tighter 15-18 set.

Completeness4/5

The surface covers design system retrieval, component/section/recipe specs, screens and flows, search, audit/tools, icon assets, and drift/history of measured design tokens, leaving few cap gaps for the declared domain. Minor gaps remain around some metadata like direct screenshot banding by product, but no major dead-end workflow is apparent.

Resources

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