Mozaika

list_design_changes

What actually changed in the web's design systems lately — the nightly Drift Ledger feed. Mozaika re-measures ~100 of the most-referenced products every night and records a dated row per product even when nothing moved, so this is a real time series, not a guess: how many products held every token, which ones shipped a change that stuck (with before/after values and the date), and which design tokens move most often across the web.

Use it to answer "does anyone actually redesign?", to ground a claim about design churn with
a citable measurement, or to spot that a reference you rely on has moved. For one product,
call get_design_drift(domain).

Args:
    limit: how many confirmed changes to return (1-40, default 10).
Free.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No

TDQS

A4.7/5.0

Behavior4/5

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

With no annotations present, the description carries the transparency burden and does well: it discloses the nightly re-measurement cadence, the ~100 product coverage, the fact that rows are recorded even when nothing changes, and the before/after value semantics. However, it does not explicitly describe the exact response structure or any authentication/rate-limit behavior, so it falls just short of a perfect score.

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 and front-loaded, starting with a clear purpose statement. It is slightly verbose with marketing-style language such as 'real time series, not a guess' and ends with a standalone 'Free.' that adds little operational value, but overall it remains focused and readable.

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?

Given the tool's low complexity (one optional parameter, no annotations, no output schema), the description is remarkably complete. It explains the data source, update frequency, what the returned changes include, and how to use the tool versus its sibling. There is enough context for an agent to invoke it 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 provides only a default value for `limit` with 0% description coverage, but the description fully compensates by defining it as 'how many confirmed changes to return (1-40, default 10).' This adds both the valid range and the semantic meaning, which is exactly what the agent needs.

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 identifies the tool as a listing function for design-system changes aggregated across ~100 products via the 'nightly Drift Ledger feed.' It uses specific language like 'what actually changed' and describes the time-series data, which distinguishes it from single-product tools such as get_design_drift.

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?

The description explicitly states when to use the tool: to answer 'does anyone actually redesign?', ground claims about design churn, or spot moved references. It also provides a clear alternative for single-product queries: 'For one product, call get_design_drift(domain).' This is strong usage 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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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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