Mozaika

compare_components

See how the BEST products each build the same UI atom — one call, cross-product, each with its anatomy read from the live DOM. Use before building any component: compare_components("Button") returns a ranked panel (one per product) of real buttons, each with measured padding / radius / border / shadow / weight / hover — so you see the real divergence (Linear's pill+shadow vs Vercel's shadowless pill vs Supabase's 6px vs Mercury's 32px) instead of guessing.

Args:
    component_type: e.g. "Button", "Navigation", "Card", "Pricing Card", "Input", "Toggle".
    industry: optional filter, e.g. "Dev Tools", "Fintech".
    scheme: optional "dark" or "light" (matches the component's measured background).
    limit: panel size (1-12, default 8).
Returns available component types if the requested one has no matches.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`scheme`	No
`industry`	No
`component_type`	Yes

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the burden and discloses key behaviors: it reads the live DOM, produces a ranked panel, and falls back to returning available types if no matches. It mentions real-world divergence examples, but doesn't cover potential performance or freshness caveats, so it's not a 5.

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

Conciseness4/5

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

The description is well-structured with a hook, usage directive, example, and parameter list. It's slightly verbose due to the brand-specific examples, but each sentence adds value and the arg list is clean.

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

Completeness4/5

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

Since there's no output schema, the description explains return values: a ranked panel with measured attributes, and fallback to available types. It covers purpose, parameters, and behavior, but doesn't mention prerequisites or edge cases like invalid inputs, making it complete but not exhaustive.

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?

Schema description coverage is 0%, so the description fully documents all four parameters: component_type with examples, industry as an optional filter, scheme with dark/light and matching measured background, and limit with range/default. This exceeds what the schema provides and adds semantic meaning.

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 the tool's function: comparing how the best products build the same UI atom, with results from live DOM measurements. It distinguishes itself from siblings by emphasizing cross-product comparison and specific measured attributes (padding, radius, etc.), which is not apparent in sibling names like get_component or compare_recipes.

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

Usage Guidelines4/5

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

The description provides explicit usage guidance: 'Use before building any component' and gives examples of component types. It lacks explicit when-not-to-use or alternative tool mentions, but the context is clear enough to infer appropriate usage.

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.

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