MotionLint

Name: MotionLint
Author: bobaba99

by bobaba99version 0.2.1i★ –iRuns on your computer

Registry nameio.github.bobaba99/motionlint

Install

Overview

Description from the maker (original English)

Catch bad animations before they ship. Deterministic motion audit + vision-LLM design review.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Design, images & audio
You need to fill in: ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY, OPENAI_BASE_URL

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "motionlint": {
      "command": "npx",
      "args": [
        "-y",
        "motionlint",
        "mcp"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value",
        "OPENAI_API_KEY": "your-value",
        "GOOGLE_API_KEY": "your-value",
        "OPENAI_BASE_URL": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add motionlint --env ANTHROPIC_API_KEY=your-value --env OPENAI_API_KEY=your-value --env GOOGLE_API_KEY=your-value --env OPENAI_BASE_URL=your-value -- npx -y motionlint mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "motionlint": {
      "command": "npx",
      "args": [
        "-y",
        "motionlint",
        "mcp"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value",
        "OPENAI_API_KEY": "your-value",
        "GOOGLE_API_KEY": "your-value",
        "OPENAI_BASE_URL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "motionlint": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "motionlint",
        "mcp"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value",
        "OPENAI_API_KEY": "your-value",
        "GOOGLE_API_KEY": "your-value",
        "OPENAI_BASE_URL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

ANTHROPIC_API_KEY secret env
Anthropic API key for vision-LLM review. Optional — the deterministic audit needs no key, and Ollama, OpenAI or Google can be used instead.
OPENAI_API_KEY secret env
OpenAI API key for vision-LLM review. Optional.
GOOGLE_API_KEY secret env
Google API key for vision-LLM review. Optional.
OPENAI_BASE_URL env
Override the OpenAI base URL to target an OpenAI-compatible endpoint. Optional.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account bobaba99, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Design, images & audioi

More stars than 0% of the other 2186 servers in this category
Published to the registry more recently than 18% of the other 2186 servers in this category
Basic checks equal to or better than 100% of the other 2186 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is MotionLint safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.2.1) reached the registry 3 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Figma MCP Server

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

MotionLint

Score any page's animation quality in one command. No API key, no config.

npx motionlint audit http://localhost:3000 --open

Deterministic — measured from the live page, no LLM involved. One-time prerequisite: npx playwright install chromium .

MotionLint measures the motion your app actually ships — durations, easing curves, stagger intervals, exit timing, reduced-motion support — and scores it against a published set of animation standards. Ease-in on a dropdown, a 600ms modal, a card that scales from 0, hover motion that fires on touch: all caught, all with the measured value and a concrete fix.

The audit is free and offline. Add an API key and MotionLint also does vision-LLM design review — multi-viewport screenshots and 50ms frame bursts of real user journeys, judged by a model and handed back to your coding agent as ranked findings. It runs as an MCP server inside Claude Code and Cursor.

Why this exists

AI coding agents read JSX, HTML, and CSS — they're blind to what the user actually sees, clicks, and watches animate. Rules in a prompt tell the agent what *should* happen; nothing checks what *did*. Modals that should slide in just pop; loading states get omitted; focus rings disappear. Code review can't catch any of this before merge, because none of it is visible in the diff.

MotionLint closes that loop: it measures the running app and feeds the verdict back.

How it's different

	MotionLint	Visual regression tools (Percy, Chromatic, Playwright snapshots)	AI design generators (v0, Galileo, Claude Design, Stitch)
Deterministic motion audit	13 checks, measured from the live page — no API key, $0	✗	✗
Multi-viewport UX review	ranked findings across 12 dimensions	pixel diffs only	generates new layouts from prompts
Animation review	50ms frame bursts via CDP screencast → contact sheet → LLM	✗	✗
Live animation tuning	Shadow-DOM previews + sliders + Claude Code export	✗	generates new motion, doesn't tune what's there
Native MCP server	✓ stdio MCP for Claude Code / Cursor	✗	varies
CI gate	✓ SARIF + exit codes for code scanning	✓ image diff thresholds	✗
Validated quality	100% recall on a 24-fixture stress test, across 5 frontier models	n/a	n/a

The conceptual gap MotionLint closes: visual-regression tools catch what *changed* but not whether the new pixels are *good*; AI design tools generate from scratch but don't review what's already running. MotionLint reviews live behavior with a vision LLM and feeds the verdict back into the coding loop.

Start here — no API key needed

npx playwright install chromium          # one-time per machine (~300MB)

npx motionlint audit http://localhost:3000 --open

That's the whole setup for the audit. It's deterministic, runs offline, costs nothing, and works on any URL you can load — your dev server, a staging deploy, or someone else's site. Requires Node 18+.

The rules it checks are published in docs/STANDARDS.md — read them before you install anything.

Then: LLM design review

Set one API key (ANTHROPIC_API_KEY, OPENAI_API_KEY, or GOOGLE_API_KEY — or run Ollama locally for free) and three more commands unlock:

npm install -g motionlint

Multi-viewport UX review of a page → ranked findings across 12 dimensions.

motionlint review http://localhost:3000

Animation review of a scripted user journey → frame contact sheet + report.

motionlint flow --spec flows/signup.json

Interactive HTML tuner — every animation on the page, with live sliders.

motionlint tune http://localhost:3000

Inside Claude Code / Cursor

claude mcp add motionlint -- npx -y motionlint mcp

Full flag surface — CI gates, route discovery, Storybook, dark mode, baselines

# CI mode — non-zero exit on critical issues, SARIF output for code scanning.

motionlint review https://staging.acme.dev --ci --threshold critical --format sarif -o ux.sarif

Polished, shareable HTML review with embedded screenshots + before/after fixes.

motionlint review http://localhost:3000 --format html -o review.html

Review every route the site knows about (sitemap.xml + Next.js app/ directory).

motionlint review http://localhost:3000 --discover-routes

Storybook mode — discover stories from /index.json, review each story iframe as its own route.

motionlint review http://localhost:6006 --storybook

Color-scheme sweep — light and dark modes, plus Windows High Contrast.

motionlint review http://localhost:3000 --schemes --forced-colors --format html -o review.html

Interaction affordances — grid each element's default/hover/focus/active states.

motionlint review http://localhost:3000 --state-grid

Agent focus — keep only the top 5 findings, and only ones not seen in prior runs.

motionlint review http://localhost:3000 --max-findings 5 --new-only

Before/after comparison — PR preview vs. production baseline.

motionlint review https://pr-123.preview.example.com --against https://prod.example.com

Reviewer focus — cap the SARIF upload at 10 annotations per report.

motionlint review https://staging.acme.dev --format sarif -o ux.sarif --max-pr-annotations 10

Pick a provider explicitly (auto-detect picks the first reachable one).

motionlint review http://localhost:3000 --provider anthropic --model claude-sonnet-5

Track provider quality across runs + teach the reviewer from eval misses.

motionlint eval --provider anthropic --evolve

Package on npm: motionlint.

Sample terminal output for a flow review:

$ motionlint flow --spec flows/signup.json --provider anthropic

→ Running flow "signup-happy-path" against http://localhost:3000/signup (11 steps, 50ms intervals × 750ms window) provider: anthropic (claude-sonnet-5) capturing flow… ✓ step 1: 16 frames ✓ step 2: 16 frames ✓ step 3: 16 frames … captured 176 frames in 31s contact sheet → .motionlint/flows/signup-happy-path-…png analyzing flow… report → .motionlint/flows/signup-happy-path.md

Score: 4/10 · 3 critical findings [critical] interaction — input focus rings missing across steps 2/4/6 [critical] interaction — submit button has no pressed state [critical] loading_state — 1.4s wait with no spinner during submit

Try the demo

A multi-route TS animation showcase ships in demo/ — covering Motion One, GSAP, anime.js, @formkit/auto-animate, and lottie-web — including a cat-themed one-pager that exercises every MotionLint capability in a single URL:

node demo/server.mjs                                # http://localhost:4173

motionlint review http://localhost:4173/cat --record --embed motionlint flow --spec flows/signup.json motionlint tune http://localhost:4173/dashboard

Routes available: /, /pricing, /signup, /dashboard, /loading, /cat. Reports go to .motionlint/reports/, screenshots to .motionlint/screenshots/, videos to .motionlint/videos/.

Setup

API keys

MotionLint auto-loads a .env file from the working directory at startup:

# .env (gitignored)

ANTHROPIC_API_KEY=sk-ant-...

or

OPENAI_API_KEY=sk-...

or

GOOGLE_API_KEY=...

or run a local Ollama (no key needed) — auto-detected on http://localhost:11434

Real environment variables take precedence over .env. With no key set and no Ollama running, MotionLint falls back to a deterministic mock provider so the full pipeline (capture → analysis → report) still runs end-to-end for smoke tests.

Provider auto-detect

MotionLint auto-detects in this order: Ollama (local) → Anthropic → OpenAI → Google. The first one with a working API key (or running service) wins. Override with --provider and --model . See Providers in depth for the per-provider quality scorecard and how to pick.

*Everything below is for readers who want to understand how MotionLint works under the hood, pick the right provider for their workflow, or wire it into CI.*

Validated quality across providers

The flow-review pipeline was stress-tested across 12 popular web-app animation patterns × 2 variants (24 fixtures total) — staggered entrances, hover/press/focus, modal entrances, loading skeletons, form errors, toasts, counter ramps, multi-animation dashboards, modal-with-content stagger, rich form feedback (focus + press + spinner + success), and scroll-driven animations (progress bar + IntersectionObserver reveal + parallax).

Run on 2026-07-27 against the current flagship from each major provider:

Provider · model	Recall (broken caught)	FPR (clean flagged)	Score gap	Wall time
OpenAI · gpt-5.6-sol	100% (12/12)	0% (0/12)	+3.3	10.9 min
OpenAI · gpt-5.5	100% (12/12)	0% (0/12)	+3.3	11.5 min
Anthropic · claude-opus-5	100% (12/12)	8% (1/12)	+4.1	21.0 min
Google · gemini-3.6-flash	100% (12/12)	17% (2/12)	+5.1	4.9 min
Anthropic · claude-sonnet-5	100% (12/12)	33% (4/12)	+3.1	10.8 min

Read this as: recall is no longer a differentiator. Every current flagship catches all 12 seeded faults. That is the finding — a year ago it wasn't true, and it means the model choice no longer decides whether MotionLint works. Pick on cost and latency.

Do not rank these models on the FPR column. A single 24-fixture run cannot resolve it. Across two clean runs of the identical suite, with nothing changed but sampling, FPR moved by 1–2 fixtures per model — gpt-5.5 1/12 → 0/12, gpt-5.6-sol 2/12 → 0/12, gemini-3.6-flash 3/12 → 2/12. One fixture is 8 percentage points, so the entire spread between "0%" and "17%" sits inside the noise floor. Treat the column as *"all of these occasionally flag something clean"*, not as a ranking.

Why the older numbers in this table's history were wrong

The first 2026-07-27 run of this suite put claude-opus-5 at 83% recall — last among all five models — and the 2026-04-29 edition of this table reported several models at 0% FPR. Both were artifacts of a MotionLint bug, not model behaviour.

Anthropic's max_tokens defaulted to 4096. Verbose responses hit the ceiling mid-JSON, and the unparseable result was scored as 0/10, no issues found — indistinguishable from a clean review. Two of Opus 5's three truncations landed on broken fixtures, which produced the entire 83% figure.

The same bug deflated FPR everywhere: a truncated review reports *nothing*, so it cannot raise a false positive. Any historical "0% FPR" was partly measuring broken parsing rather than model precision. Fixed 2026-07-27, along with the sibling paths that turned truncated, refused, and safety-blocked responses into clean-looking results.

Full per-provider scorecards in .motionlint/stress/ after running scripts/run-all-benchmarks.mjs. Use --only : to re-run a single model.

Providers in depth

Provider	Model	Setup	Quality (24 fixtures)	Cost per review¹

| google | gemini-3.6-flash | `GOOGLE_API

Only the beginning is shown; read the rest in the repo

MotionLint

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Design, images & audioi

Common questions

Alternatives

Figma Context MCP

Claude Real Video

Figma MCP Server

README from the maker

MotionLint

Why this exists

How it's different

Start here — no API key needed

Then: LLM design review

Multi-viewport UX review of a page → ranked findings across 12 dimensions.

Animation review of a scripted user journey → frame contact sheet + report.

Interactive HTML tuner — every animation on the page, with live sliders.

Inside Claude Code / Cursor

Polished, shareable HTML review with embedded screenshots + before/after fixes.

Review every route the site knows about (sitemap.xml + Next.js app/ directory).

Storybook mode — discover stories from /index.json, review each story iframe as its own route.

Color-scheme sweep — light and dark modes, plus Windows High Contrast.

Interaction affordances — grid each element's default/hover/focus/active states.

Agent focus — keep only the top 5 findings, and only ones not seen in prior runs.

Before/after comparison — PR preview vs. production baseline.

Reviewer focus — cap the SARIF upload at 10 annotations per report.

Pick a provider explicitly (auto-detect picks the first reachable one).

Track provider quality across runs + teach the reviewer from eval misses.

Try the demo

Setup

API keys

or

or

or run a local Ollama (no key needed) — auto-detected on http://localhost:11434

Provider auto-detect

Validated quality across providers

Providers in depth