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Designesy

by io.github.LE-VAI

designesy_monitor_score

Score a URL for continuous design-drift governance — the temporal layer over the drift radar. Re-runs the 12 drift checks (d01-d12) on the URL and computes 10 monitor checks (m01-m10): schedule registered, last run fresh, drift delta vs baseline, trend slope (3-run trajectory), new violations since last run, resolved since last run (the healing signal), score degradation threshold, token-set mutation, contract version drift, and alert delivered. When alerts fire and an email address is provided, sends an HTML drift-alert email via Resend (requires RESEND_API_KEY env var). Pass a history array of prior snapshots to compute deltas; omit it for a first-run baseline. Use this to watch a design system over time — "weekly audits at cents per report" (Into Design Systems 2026). When NOT to use: for a single point-in-time drift check, use designesy_drift_score; for design-contract scoring, use designesy_score. Executable — fetches the URL, extracts CSS + :root tokens, runs checks, computes deltas. No browser needed. Returns JSON: { ok, url, score (0-100, governance health), grade (A-F), pass, warn, fail, total, currentSnapshot, baseline, previous, driftChecks, monitorChecks, alerts, emailAlert }. Results cached ~24h per URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoURL to monitor for drift. Defaults to https://www.designesy.org/ if not provided.
emailNoEmail address to receive drift alerts. When alerts fire AND this is provided AND RESEND_API_KEY is set, an HTML alert email is sent. Optional — without it, alerts surface in-UI only.
historyNoPrior snapshots for delta computation. Omit for a first-run baseline.

TDQS

A4.5/5.0
Behavior5/5

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

Despite having no annotations, the description discloses execution behavior: it fetches the URL, extracts CSS tokens, runs checks, and computes deltas; requires RESEND_API_KEY for email alerts; caches results for 24h; and sends emails as a side effect when conditions are met. This fully carries the transparency burden.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is lengthy but front-loaded with the core purpose, then details checks, side effects, and alternatives. While it contains a marketing quote and a long enumeration of monitor checks, the detail is warranted for a complex tool, though slightly verbose.

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?

The description is highly complete: it explains the JSON return shape (compensating for no output schema), covers prerequisites, side effects, caching, and usage distinctions. No significant contextual gaps remain for the complexity level.

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?

The input schema already covers all 3 parameters with rich descriptions (100% coverage), including defaults and conditional email sending. The description adds marginal meaning by restating the history parameter's purpose, so it doesn't improve upon the schema baseline.

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 clearly states the tool scores a URL for continuous design-drift governance, explicitly distinguishing it from single-point-in-time drift checks (designesy_drift_score) and design-contract scoring (designesy_score). This provides a specific verb, resource, and temporal 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?

It explicitly states when to use (watch a design system over time) and when not to use, naming exact alternative tools for point-in-time drift checks and contract scoring. This is exemplary usage guidance that leaves no ambiguity.

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.

Resources