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Designesy

by io.github.LE-VAI
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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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. 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
  2. First observed

TDQS

A4.5/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden and meets it: it discloses server-side fetching, ~24h caching per URL, the MANUAL status for browser-dependent checks and the /api/score/audit escape hatch, and the SKIP (N/A) semantics for inapplicable checks. These are non-obvious behaviors an agent could not infer from the schema.

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 execution behavior, then return shape; every section earns its place. The parenthetical category list and status explanations add length that is mostly justified for a 42-check engine, though the format sentence is wasted given it is not a real parameter.

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?

With no output schema and no annotations, the description steps in with a full return JSON sketch (url, score, grade, pass_count, fail_count, checks[]), status semantics, and caching behavior. The only gap is the phantom 'format' parameter, which makes the tool look richer than its schema allows.

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% for the single 'url' parameter, so the baseline is 3. The description corroborates the default URL behavior but then instructs 'Pass format="canonical"' — a parameter that does not exist in the input schema, which risks an agent emitting an invalid call.

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 and resource ('Score a live URL against the Designesy design contract'), quantifies the engine (42-check deterministic) and enumerates what it audits (tokens, motion, accessibility, cadence, takt, typography, copywriting). It also names the sibling tools it is not, so an agent can route without opening schemas.

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?

Explicit 'When NOT to use' block with three named alternatives and the condition selecting each (designesy_tokens_score for token files, designesy_motion_score for Lottie, designesy_design_review for qualitative critique). This is exactly the routing guidance the dimension rewards.

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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