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Designesy

by LE-VAI
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TypeScript
Remote

Monitor drift with email alerts

designesy_monitor_score

Track design-system drift over time by scoring a URL, computing deltas from prior snapshots, and alerting when governance thresholds breach.

Instructions

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 server-side per URL.

Input 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.13.8

TDQS

A4.8/5.0
Behavior5/5

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

Goes well beyond the annotations: it discloses that the tool executes and fetches the URL, extracts CSS and :root tokens, sends an HTML email via Resend only when alerts fire AND email is given AND RESEND_API_KEY is set, requires no browser, and caches results ~24h server-side. These are real behavioral traits (auth dependency, side effect, caching) not derivable from the hints, and readOnlyHint=false / non-idempotent is consistent with a tool that emails and re-scores.

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?

Effectively one dense paragraph but front-loaded: purpose, then mechanics, then when-not-to-use, then return shape. Nearly every clause carries information, though the marketing quote ('weekly audits at cents per report' — Into Design Systems 2026) is decorative padding that could be cut.

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?

With no output schema, the description compensates by enumerating the returned JSON keys (ok, url, score, grade, pass/warn/fail/total, snapshots, driftChecks, monitorChecks, alerts, emailAlert) and the server-side caching behavior. An agent has everything needed to call it and interpret the result.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, and the description adds genuine meaning for history ('prior snapshots for delta computation; omit for a first-run baseline') and reinforces the conditional email behavior. It adds usable semantics beyond the schema but does not, for example, explain the expected shape/ordering of the history snapshots.

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?

Opens with a specific verb+resource ('Score a URL for continuous design-drift governance') and immediately frames its scope as 'the temporal layer over the drift radar', distinguishing it from the point-in-time siblings. It enumerates the concrete checks (d01-d12, m01-m10) so an agent knows exactly what work happens.

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 positive guidance ('watch a design system over time', pass history for deltas vs omit for a baseline) plus a dedicated 'When NOT to use' clause naming the alternatives designesy_drift_score and designesy_score with their selecting conditions. This is the textbook routing pattern.

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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