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designesy_compare

Diff two design systems from live URLs — the only URL-scoped design-token diff engine. Fetches both URLs in parallel, extracts their :root custom properties, and produces a structured diff across 8 dimensions: tokens added (in A not B), removed (in B not A), renamed (heuristic Levenshtein ≤ 2), value-changed (same name, different value), scale-stop-changed (spacing/radius/color scale steps), contrast-drift-per-pair (WCAG contrast ratio change for shared color tokens), structure-delta (token count + category distribution), and score-delta (runs /score on both URLs and diffs). Use this to answer "what actually changed between two design systems" or "how does our design system differ from a reference". When NOT to use: for single-site drift detection, use designesy_drift_score; for continuous monitoring, use designesy_monitor_score. Executable — fetches both URLs, extracts CSS + tokens, computes diff. No browser needed. Returns JSON: { ok, urlA, urlB, score (0-100, diff completeness), grade, pass, warn, fail, total, tokensA, tokensB, added[], removed[], renamed[], valueChanged[], scaleDiff, structureDelta, contrastDrift[], scoreDelta, checks[] }. Results cached ~24h per URL pair.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlAYesFirst URL to compare (e.g. your design system).
urlBYesSecond URL to compare (e.g. a reference or competitor).

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description transparently discloses key behaviors: fetches both URLs in parallel, extracts :root custom properties, computes a structured diff, runs /score on both URLs, does not require a browser, and caches results for ~24h. This goes well beyond the tool name and schema.

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?

The description is dense but well-organized: purpose first, then dimensions, use cases, alternatives, execution details, and return shape. Every sentence adds concrete information, with no fluff or repetition of the schema.

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 no output schema, the description includes the full JSON return structure and enumerates all computed diff categories. It also covers practical aspects like caching and that no browser is needed, making it highly complete for an AI agent to invoke and interpret results.

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

Parameters4/5

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

Schema already provides descriptions for both urlA and urlB, giving 100% coverage. The description enhances these by clarifying the directional meaning of A and B in the diff (e.g., 'added (in A not B), removed (in B not A)') and noting caching per URL pair, adding value over 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?

Description opens with 'Diff two design systems from live URLs', using a specific verb and resource. It further differentiates from siblings by calling itself 'the only URL-scoped design-token diff engine' and details the 8 diff dimensions, making the tool's scope unmistakable.

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?

Provides explicit use cases: 'what actually changed between two design systems' or 'how does our design system differ from a reference'. Also gives clear when-not-to-use guidance, pointing to designesy_drift_score for single-site drift and designesy_monitor_score for continuous monitoring.

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

A4.7/5.0
Disambiguation5/5

Each tool has a clearly scoped purpose, and the extensive 'When NOT to use' notices cleanly separate the many scoring variants (e.g., score, drift, readiness, monitor, tokens, motion, a11y). Even similar informational endpoints (contract, skill, llms) are differentiated by format and use case. No two tools appear to do the same thing.

Naming Consistency5/5

All tools follow a consistent 'designesy_' prefix, and scoring tools uniformly append '_score' (e.g., drift_score, tokens_score, monitor_score). Non-score tools use descriptive noun suffixes (catalog, contract, report, guardrails). The pattern is predictable and uniform throughout.

Tool Count4/5

At 17 tools, the set is slightly above the ideal 3-15 range, but the breadth of the design-system intelligence domain justifies the count. Each scoring variant targets a different artifact (live URL, token file, Lottie, temporal drift) and the informational endpoints serve distinct formats. The tool count is heavy but not bloated.

Completeness5/5

The toolset covers the full assessment lifecycle: full audit (score), drift and temporal governance (drift_score, monitor_score), AI readiness (readiness_score), token and motion validation (tokens_score, motion_score), accessibility framework (a11y_score), diff (compare), composite report (report), guardrails generation, and multiple discovery formats (catalog, contract, skill_md, llms). No obvious dead ends exist; each tool leads to a usable artifact or clear next step.