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Designesy

by io.github.LE-VAI

designesy_score

Score a live URL against the Designesy design contract — a deterministic 42-check verification engine that returns a numeric score, letter grade (A–F), and per-check breakdown. Use this to audit whether a website or AI-generated UI complies with a real design contract (tokens, motion, accessibility, cadence, takt, typography, copywriting). When NOT to use: for token-file validation only, use designesy_tokens_score; for a Lottie file, use designesy_motion_score; for a qualitative critique, use designesy_design_review. Executable — fetches the URL server-side, extracts CSS, runs 42 checks. Results cached ~24h per URL. Checks needing a live browser (Core Web Vitals, sound toggle, overflow) return MANUAL, not FAIL — run the full audit (/api/score/audit) to resolve them. Checks that are not applicable to the site (no tokens, no buttons, no DESIGN.md) return SKIP (N/A). Returns JSON: { url, score (0–100), grade (A–F), pass_count, fail_count, checks[{id, name, status, weight, category}] }. Pass format="canonical" for review-findings.json schema, "review" for markdown, or "google" for design.md-compatible output.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoURL to score. Defaults to https://www.designesy.org/ if not provided.

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations provided, the description carries full behavioral disclosure and does so thoroughly. It states the executable server-side fetching behavior, 24-hour caching, MANUAL status for live-browser-dependent checks, SKIP for non-applicable checks, and the available output formats — all beyond what the schema could convey.

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 dense and well-organized, front-loading purpose and output while using a clear logical progression: contract, usage, exclusions, execution behavior, statuses, return shape, and format options. It is longer than minimal, but each clause earns its place; only a slight redundancy between the opening 'returns a numeric score' and the later 'Returns JSON' prevents a 5.

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?

Despite having no output schema, the description fully enumerates the response structure, status semantics, caching behavior, and format switches. It even points to the full audit path for resolving MANUAL checks, leaving no critical knowledge gap for an agent to correctly invoke and interpret the tool.

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

Parameters3/5

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

Schema description coverage is 100%, and the URL parameter is already clearly documented in the schema. The description adds only the default URL behavior and the format options, which are useful but not essential for understanding the single parameter. This meets the baseline for full schema coverage without adding significant new 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 opens with a precise verb and resource: 'Score a live URL against the Designesy design contract,' and explains the verification engine's 42-check nature and output. It explicitly distinguishes itself from siblings by naming designesy_tokens_score, designesy_motion_score, and designesy_design_review, so an agent can select it correctly even among 16 related tools.

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 gives direct usage context ('Use this to audit whether a website or AI-generated UI complies with a real design contract') and a dedicated 'When NOT to use' section naming the exact alternative for token files, Lottie files, and qualitative reviews. This makes both selection and exclusion criteria explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Designesy toolsInspect
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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.

Resources