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Designesy

by io.github.LE-VAI
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Score URL against contract

designesy_score
Read-onlyIdempotent

Score a live URL against the Designesy design contract: a deterministic 45-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 45 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). The default format returns the engine's JSON: { score (0-100), grade (A-F), pass, warn, fail, skip, manual, total, checks[{id, item, category, status, detail, remediation, weight}], categoryScores, contractVersion, receipt }. Pass format="canonical" for review-findings.json schema, "review" for markdown, or "google" for design.md-compatible output. Pass scope="contract" or "universal" to set the scoring scope instead of auto-detecting it.

Input Schema

NameRequiredDescriptionDefault
urlNoURL to score. Defaults to https://www.designesy.org/ if not provided.
scopeNoScoring scope. contract: every check counts absence against the site. universal: optional features and Designesy-specific token checks return SKIP when absent. Omit to auto-detect: contract for designesy.org, universal for every other site.
formatNoOutput format. designesy (default): the native JSON. canonical: the review-findings.json schema. review: a markdown report. google: the @google/design.md-compatible JSON.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / format
      Added value: +{
+  "description": "Output format. designesy (default): the native JSON. canonical: the review-findings.json schema. review: a markdown report. google: the @google/design.md-compatible JSON.",
+  "enum": [
+    "designesy",
+    "canonical",
+    "review",
+    "google"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / scope
      Added value: +{
+  "description": "Scoring scope. contract: every check counts absence against the site. universal: optional features and Designesy-specific token checks return SKIP when absent. Omit to auto-detect: contract for designesy.org, universal for every other site.",
+  "enum": [
+    "contract",
+    "universal"
+  ],
+  "type": "string"
+}
  2. Changed2 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / additionalProperties
      Removed value: -false
  3. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint), and the description adds substantial operational context: server-side fetch, ~24h per-URL caching, MANUAL vs SKIP vs FAIL semantics, and the need to run /api/score/audit to resolve browser-dependent checks. Its main weakness is that the caching statement is the only qualifier; no rate limits, auth needs, or failure modes for unreachable URLs are given.

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?

Front-loaded with purpose, then exclusions, then behavior, then output shape — a sensible order with dense, earned sentences. It loses a point for re-describing the format and scope enums that the schema already documents, adding tail 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?

There is no output schema, so the description must (and does) explain the return payload key-by-key (score, grade, pass/warn/fail/skip/manual/total, checks[], categoryScores, receipt) and the alternative shapes for canonical/review/google.

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 coverage is 100% and both enums are fully documented in the input schema, so the parameter layer already carries the load; the description's restatement of format and scope values is largely redundant. Setting baseline 3 is correct here — it adds no syntax or semantics the schema lacks.

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?

States a specific verb+resource ('Score a live URL against the Designesy design contract') and quantifies the engine (45 checks, numeric score, letter grade, per-check breakdown). It explicitly names the sibling tools it is not (designesy_tokens_score, designesy_motion_score, designesy_design_review), so the agent can differentiate without opening any schema.

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?

Contains an explicit 'When NOT to use' block routing to three named alternatives with their selection conditions (token-file validation, Lottie file, qualitative critique). Usage is unambiguous and requires no inference.

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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