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Designesy

by io.github.LE-VAI
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Score UI drift

designesy_drift_score
Read-onlyIdempotent

Score a live URL for AI-generated UI drift. Its 12 checks detect the four documented 2026 drift failure modes: token fabrication (var() references with no fallback to custom properties declared nowhere: not in a stylesheet, a block or a style attribute), within-session drift (spacing/color/radius value variance), between-session amnesia (inconsistent font stacks, shadows, transitions), and silent breaking changes (z-index chaos, alias chains that end in an undeclared property). Use this when you need to verify whether a site (especially an AI-generated one) is drifting off its own declared token system. When NOT to use: for a full 45-check design-contract score, use designesy_score; for token-file format validation, use designesy_tokens_score. Executable: fetches the URL server-side, extracts all CSS (inline + linked stylesheets), parses custom property declarations (stylesheets, blocks and style attributes) and var() references, runs 12 drift checks. No browser needed. Returns JSON: { ok, url, scope, score (0-100), grade (A-F), pass, warn, fail, skip, total, tokensExtracted, checks[{id, item, category, status, detail}] }. Results cached ~24h per URL.

Input Schema

NameRequiredDescriptionDefault
urlNoURL to scan for drift. 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. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive), and the description adds genuinely new behavior: server-side fetch, CSS extraction from inline/linked sources, no browser needed, and ~24h per-URL caching. It also discloses the return shape in the absence of an output 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?

Well front-loaded: purpose and drift-mode detail come first, then when-to-use, exclusions, execution model, and return shape. It is dense but each block is informative; the failure-mode parentheticals are slightly verbose for the value they add.

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?

For a complex, zero-required-param analyzer with no output schema, the description supplies everything an agent needs: scope, execution model, caching, and a full inline specification of the returned JSON keys. No material gaps remain.

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 single url parameter with its default is fully documented in the schema. The description adds no syntax, format, or constraint detail beyond what the schema already provides, so the baseline 3 applies.

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 for AI-generated UI drift') and enumerates the four failure modes the 12 checks target. It cleanly distinguishes itself from siblings like designesy_score and designesy_tokens_score by scope.

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?

Explicitly names the trigger ('when you need to verify whether a site is drifting off its own declared token system') and provides explicit exclusions routing to designesy_score (45-check contract) and designesy_tokens_score (token-file format). Nothing is left to 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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