About uxloom
uxloom is an MCP server and Agent Skills package for validating UI/UX designs as journey state machines. It helps design and development teams find missing states, accessibility issues, and implementation drift before release, with deterministic checks suited to CI.
Key features
- 01Journey and screen state validation
- 02WCAG contrast and touch-target checks
- 03Localization text expansion checks
- 04Design preview and HTML export
- 05CI checks with JSON and SARIF output
Use cases
- 01Find missing error, empty, or loading states
- 02Check journey reachability before implementation
- 03Gate design completeness in CI
- 04Review implementation drift against design contracts
MCP support
The MCP server exposes design setup, journey and screen definition, validation, and coverage workflows to MCP clients. Agent Skills are included.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
npx -y uxloomUXLOOM_PROJECTPath to the project file (default: ./uxloom.project.json in the working directory)Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEUXLoom
Your generator gave you 6 screens. UXLoom proves you're missing 9 states.
AI generators (v0, Lovable, Figma Make, Claude) produce happy-path screens. UXLoom is the critic layer: it models user journeys as state machines, treats screens as nodes with state contracts, and mechanically proves what's missing before a line of production code exists — unreachable screens, dead ends, missing error/empty/loading states, WCAG contrast failures, undersized touch targets, and labels that will overflow under localization.
Agent-native by design: the interface is an MCP server (works with Claude Code, Codex, and any MCP client), with Agent Skills included.
Deterministic by design: same input, byte-identical report — benchmarked
(packages/bench) at 1.000 precision/recall on a seeded
defect catalog, SHA-256-stable across processes, 1000 screens in under 5ms.
That's what lets design completeness gate CI, where an LLM opinion can't.
Website: uxloom.dev · npm: uxloom · MCP registry: io.github.uxloom-dev/uxloom ·

Packages
| Package | What it is |
|---|---|
@uxloom/journeygraph | The open design-as-data format: journeys as state machines, screens as nodes with required states |
@uxloom/critics | The validators: journey completeness, state coverage, WCAG contrast, touch targets, text expansion |
uxloom | The MCP server + Agent Skills — the interface agents use |
New here? Start with the Quickstart — prerequisites, the Claude Code walkthrough, what to say to your agent, and troubleshooting.

Quick start (agents)
# Claude Code
claude mcp add uxloom -- npx -y uxloom
# Codex CLI
codex mcp add uxloom -- npx -y uxloom
The project file (uxloom.project.json) lives in your workspace and belongs
in git — the design is data, versioned next to the code it specifies.
Quick start (humans & CI)
npx uxloom init # one-command setup: MCP config + agent skill + starter file
npx uxloom preview # live mocks (themed, commentable, EDITABLE in the browser)
npx uxloom export # shareable HTML — plus --svg (Figma/Penpot import; add
# --manifest for the round-trip key) and --png (playwright)
npx uxloom check # design completeness — exit 1 on errors, CI-ready
npx uxloom audit # implementation drift — web AND native (Swift/Kotlin/
# Dart/Java markers); --live verifies the real DOM;
# --design <file|dir> audits a Figma/Penpot export vs the contract
npx uxloom diff # human-readable design diffs for PR review
Evidence-based design: every decision in the contract can carry its
rationale — reasoning, rejected alternatives with pros/cons, sources,
confidence — enforced by the critics once adopted, iterated through a
bounded design_review loop (max 3 rounds), and shown to stakeholders in
the preview's evidence panel (ⓘ) and exports. The design doesn't just
validate; it argues its case.
Agent-addressable comments: a reviewer drops a pinned comment in the
preview and clicks "→ agent". The comment becomes a work item any Gen-AI
model can read with full context — comment_context returns the pinned
layout block, the screen contract, the journey references, and the current
findings for that screen — act on, and resolve back into the preview with
a note. One click from feedback to addressed.

CI-native: check and audit take --json, --sarif (GitHub code
scanning), and --github (inline PR annotations). Brownfield-ready:
--update-baseline freezes existing findings so only new drift blocks;
uxloom.config.json tunes thresholds to your accessibility bar. Full
documentation: uxloom.dev/docs.html.

Add it to CI and a happy-path-only design can never merge:
- run: npx uxloom check design/uxloom.project.json
Workflow (also shipped as a skill in packages/mcp-server/skills/):
project_init → brief_start/brief_answer → journey_define →
screen_register → project_validate → fix → repeat until zero errors →
coverage_report.
Does it actually catch things?
tools/dogfood.mjs drives the real MCP server through three products, twice
each: screens as a happy-path generator hands them over, then repaired using
the validation report. Artifacts in examples/.
| Product | Generated (happy-path) | Repaired |
|---|---|---|
shopmweb — e-commerce checkout (mWeb + Android) | 9 errors, 6 warnings | 0 / 0 |
taskflow — SaaS signup/onboarding (web) | 1 error, 6 warnings | 0 / 0 |
ridenow — ride booking (iOS + Android, offline-heavy) | 3 errors, 7 warnings | 0 / 0 |
Caught: an unreachable promo screen, dead-end verification states, five
undesigned payment/error states, a 2.4:1 contrast button, a 40px touch target
on Android, a checkout label that breaks in German, and three products' worth
of missing offline states. Zero errors and zero warnings is reachable
honestly — screens declare documented exemptions where a baseline state
genuinely cannot apply, and contradictory exemptions are flagged.
Development
npm install
npm run typecheck
npm test
Status
Released and maintained: on npm and
the official MCP registry, with the benchmark scorecard published in every
GitHub release. The
JourneyGraph format (formatVersion: "0.1") may evolve until 1.0; releases
follow RELEASING.md — every surface is drift-checked in CI.
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.