Raven MCP

Odin's ravens brought back knowledge of the world — Raven brings back design intelligence.

A design knowledge MCP server that Claude can query when generating UI. Eight layers: principles, patterns, content design systems, research methods, service design, brand/visual, business strategy, and design tokens.

Raven MCP is a personal open-source project by Andrew Cunliffe. It is not endorsed by, affiliated with, or supported by Intuit Inc. or any other company referenced in its source data. See NOTICE for full attribution of upstream sources and their licenses.

What it does

Raven gives Claude access to a comprehensive design knowledge base:

• Principles — Nielsen's 10 Heuristics, all 21 Laws of UX, Gestalt principles, WCAG accessibility, typography rules, color theory, mobile UX, D4D framework, UX writing, service design, brand, color-systems (palette-size discipline), and spacing-systems (base-unit grid + scale limits)
• Patterns — Proven UI patterns for signup flows, pricing pages, navigation, forms, landing pages, dashboards, modals, empty/error/loading states, CTAs, social proof, mobile conversion — plus content patterns (error messages, empty-state copy, notifications, form validation) and service patterns (service blueprinting, human handoff, signup-as-service, omnichannel continuity, moments of truth)
• Content systems — Voice & tone guides from publicly documented brand systems: Mailchimp, GOV.UK, Shopify Polaris, and Atlassian
• Research — Qualitative, quantitative, and usability methods with do/don't protocols and checklists. Metrics frameworks: HEART, AARRR/Pirate, North Star Metric, conversion funnel, RICE, OKRs.
• Service design — Service blueprinting (with HTML blueprint generation — current vs. ideal state), human-handoff patterns, signup-as-service, omnichannel continuity, moments of truth / recovery, and the GOV.UK Service Standard
• Brand & visual — Logo usage (clear space, min sizes, variants, placement, restraint), gradient usage (hierarchy, palette, contrast, trend vs signature), imagery (consistency, representation, purpose), visual hierarchy, brand-as-system, and current (2026) visual-design trends
• Business — Monetization models, retention strategies, onboarding optimization, growth mechanics, and product metrics frameworks
• Tokens — Design system tokens for Stripe, Linear, and more
• Creative studio — Local-first brand profiles, asset references, character reference profiles, provider-agnostic image/video/3D/audio generation jobs, campaign plans, and transparent creative scoring. Raven does not ship media-provider credentials; set RAVEN_CREATIVE_RUNNER to route jobs to your own renderer.

Install

Claude Code — one command

claude mcp add raven -- npx -y raven-mcp

Manual config (Claude Desktop or team .mcp.json)

{
  "mcpServers": {
    "raven": {
      "command": "npx",
      "args": ["-y", "raven-mcp"]
    }
  }
}

Claude Desktop — one-click extension

Prefer not to edit JSON? Download raven.mcpb and double-click it. Claude Desktop installs Raven automatically — no Node, no terminal.

From source

git clone https://github.com/rhinocap/raven-mcp.git
cd raven-mcp && npm install && npm run build

Tools

Creative studio

Raven now covers the creative-production workflow around media generation without copying or depending on any closed vendor. The tools are orchestration primitives:

• Store brand kits locally with create_brand_profile.
• Register product photos, logos, references, or URLs with register_creative_asset.
• Create character/identity reference sets with create_character_profile.
• Generate provider-ready payloads with create_generation_job.
• Build full campaign shot lists with plan_creative_campaign.
• Score creative concepts with score_creative.

By default, jobs are saved as local draft payloads under ~/.raven/creative (override with RAVEN_CREATIVE_HOME). To run real media generation, set RAVEN_CREATIVE_RUNNER to an executable that reads one job JSON object from stdin and returns JSON on stdout. That runner can call any provider you choose; Raven never stores API keys in source.

iOS / SwiftUI audits

Raven audits native iOS apps against the Apple Human Interface Guidelines, not web/CSS conventions. None of the web-only rules (lang, title, flex-wrap, clamp, max-width, CSS custom properties, bare hex) run on iOS input — and get_checklist/get_principles take platform: "ios" to return HIG items (Dynamic Type, 44pt targets, SF Symbols, safe areas, dark-mode parity, App Review privacy) instead of the web set.

• audit_swiftui — paste SwiftUI source (source: a string or array of files). Statically flags hardcoded .font(.system(size:)) below ~13pt, tiny semantic fonts (.caption/.caption2), hardcoded Color(red:green:blue:)/hex literals (vs. asset-catalog or semantic system colors), interactive frames under 44×44pt, and ad-hoc spacing off the 4/8-pt grid. Rewards semantic Dynamic Type fonts, semantic system colors, SF Symbols, and flexible frames. Pass the optional accent_color_contents (the raw AccentColor.colorset/Contents.json) and it verifies the accent color actually defines components — catching an empty/undefined AccentColor that would silently fall back to system blue.
• audit_ios_screen — the iOS analog of audit_layout. Call with no args for the expected snapshot shape and how to capture it (Accessibility Inspector / XCUITest). Call with { elements: [{ label, rect, role, fontPt, fgColor, bgColor }], viewport } (plus an optional base64 screenshot) to score 44×44pt touch targets, contrast (with iOS secondaryLabel/tertiaryLabel treated as platform-standard — a warning, not a hard fail), and visual rhythm (alignment, gap consistency, optical balance).
• audit_ios_privacy — the "no sketchy issues" gate. Reads info_plist or an Expo app_json (managed RN apps have no Info.plist) plus optional privacy_md, entitlements, and source. Flags NS*UsageDescription strings that are vague or contradict the code (e.g. an NSHealthUpdateUsageDescription write claim that requestAuthorization(toShare: []) never fulfills), unused entitlements, Android permissions (Expo), ATS cleartext exceptions, secrets/keys shipped in the bundle or app.json extra, and default data-egress paths not disclosed at the point of choice (a pre-selected "Recommended" option that silently sends personal data to a hosted server).

All three return the same shape as audit_page — score, grade, summary, passes, errors, warnings, fix_priority (with audit_ios_screen adding a metrics block).

One command: node scripts/ios-audit.mjs <app-dir> [--snapshot snap.json] [--md report.md] discovers all the inputs and runs all three tools with an aggregated report.

React Native / Expo audits

Anyone building a React Native or Expo app gets the same treatment. RN renders to native iOS + Android widgets, so audit_ios_screen already scores its rendered output (an accessibility snapshot is platform-level); audit_rn covers the JSX/StyleSheet source — the RN analog of audit_swiftui — graded against the iOS HIG + Android Material conventions RN has to satisfy on both platforms. get_checklist/get_principles take platform: "react-native".

• audit_rn — paste RN source (source: a string or array). Flags touchables (Pressable/Touchable*) missing accessibilityLabel/accessibilityRole, touchables under 44pt with no hitSlop, allowFontScaling={false} (silently breaks Dynamic Type), fontSize below ~13, screens with no SafeAreaView/useSafeAreaInsets, and — for multi-mode apps — hardcoded colors with no useColorScheme/Appearance. Pass color_scheme: "dark"/"light" (your Expo userInterfaceStyle) and the dark-mode check is suppressed for intentionally single-mode apps. Rewards SafeAreaView, hitSlop, Platform-aware code, and a theme.
• audit_ios_privacy also accepts an Expo app_json — it audits expo.ios.infoPlist, Android permissions, plugins, and scans expo.extra/config for secrets and Google API keys.

One command: node scripts/rn-audit.mjs <app-dir> [--snapshot snap.json] [--md report.md] discovers screens + app.json (reading userInterfaceStyle so dark-only apps aren't false-flagged) and runs everything.

Responsive visibility audits

audit_responsive_visibility renders a page at multiple breakpoints (default: 390px mobile, 768px tablet, 1440px desktop, 2160px ultra-wide) and flags content elements that are visible on desktop but hidden on mobile — catching the "vanishes on mobile" bug class. Each flagged element is categorised as likely-oversight (content that shouldn't be hidden) or intentional (decorative elements). Detects hiding via CSS (hidden, display:none, opacity:0, visibility:hidden) and responsive Tailwind classes (hidden md:block, etc.).

Usage:

• audit_responsive_visibility(url) — render at default breakpoints and flag mismatches.
• audit_responsive_visibility(url, [390, 768, 1440]) — custom breakpoints.
• Optional viewportHeight (default: 900px) for tall content.

Returns flagged elements with selector, hiding class, visibility at each breakpoint, and category.

Contrast audits

audit_contrast computes WCAG contrast ratios for every text element on a rendered page, reporting AA (4.5:1 normal text, 3:1 large) and AAA (7:1 normal, 4.5:1 large) pass-fail. Useful for catching small-text / low-contrast pairs that a screenshot eyedropper would catch manually — Raven replaces the math. The background color is composited from the full ancestor stack (nearest opaque layer onward) for accurate contrast on layered UIs.

Usage:

• audit_contrast(url) — render a live page and audit all text.
• audit_contrast(dom_snapshot: [{ selector, color, bgColor, fontPx?, bold?, text? }]) — audit a pre-captured snapshot (useful for dynamic or cookie-protected pages).

Returns all text elements scored, failures highlighted with delta-to-pass, and a summary of AA/AAA failure count.

WCAG math: Contrast ratio uses linearised luminance (WCAG 2.1 § 1.4.3) — black-on-white is exactly 21, white-on-black is exactly 21. Large text (18.66pt+ bold or 24pt+) needs only 3:1 / 4.5:1 AAA; regular text needs 4.5:1 / 7:1.

Headless browser audits

audit_page can render a live URL in headless Chromium, scroll to settle reveal-on-scroll elements, and play preload=none videos before capturing — preventing false "blank section" reports caused by whileInView states that haven't fired yet.

Usage:

• Static HTML mode — pass html string for immediate static analysis (existing behavior, no change).
• Rendered URL mode — pass url (full HTTP/HTTPS URL). Raven launches Chromium, renders the page, optionally scrolls, and audits the live DOM.
  • scroll_settle: true — scroll from top to bottom in viewport-height steps, then wait 300ms for IntersectionObserver / whileInView reveals to fire. Unloaded videos (preload="none") are played to detect if they render blank. This surfaces the real rendered state and avoids false positives on reveal-on-scroll or lazy-loaded content.
  • viewport: { w, h } — set the render viewport (default: { w: 1440, h: 900 }).

Video artifacts detection: If any <video> with preload="none" (or missing preload) renders with readyState < 2 (i.e. would show a black box in a screenshot), Raven flags it as an unloaded-video-artifact in the result. This is informational — not a pass/fail — since preload=none is often intentional. On cookie-protected hosts, video requests may fail because iOS/Android media daemons don't send cookies; Raven notes this to help you troubleshoot (e.g. disable deployment protection, use a token-based bypass).

Adversarial verification: Set adversarial_verify: true to independently re-check each finding against the live DOM using a different method. Findings are tagged:

• confirmed — the finding is real on the live page (e.g. missing <title> in the rendered DOM)
• likely-artifact — the finding is an artifact of the static audit method (e.g. a <video preload="none"> rendered blank, which is expected behavior, not a missing resource)
• inconclusive — the finding cannot be independently verified (e.g. aggregate rules like color-palette size)

The result includes adversarial_verification: { debunked_count, confirmed_count, inconclusive_count }, where debunked_count is the number of likely-artifacts. This surfaces false positives so you only fix real issues. Backwards-compatible: when adversarial_verify is absent or false, the output is identical to prior versions.

Setup: First time only, run npx playwright install chromium to download the browser binary. If the binary is missing when you call audit_page with url, you'll see a clear instruction to run the install command.

Before/after design diffs

evaluate_design can now accept base64-encoded PNG screenshots to measure whether a fix actually changed the rendered output.

Usage:

• Pass before_screenshot and after_screenshot (both base64 PNGs, with or without the data:image/png;base64, prefix).
• Raven returns fix_confirmed: true if the images differ by > 0.1% of pixels (accounting for jpeg/PNG decode variance).
• changed_ratio — exact fraction of pixels that changed (0–1).
• changed_region — bounding box { x, y, w, h } of the changed pixels (null if no changes detected).
• dimensions — image-derived measurements (canvas size, brightness, color shift) as context, with the caveat that these are pixel-level proxies, not Raven principle scores.

When before/after screenshots are provided alongside a description, evaluate_design returns both the principle-based evaluation and the pixel diff. When screenshots are provided without a description, the evaluation gracefully skips the principle search and returns the diff only. Backwards-compatible: without screenshots, the tool behaves identically to prior versions.

Release updates

Raven ships new principles, patterns, and brand systems regularly. For one email per minor/major release (patches stay quiet):

• Web: ravenmcp.ai/#updates — 10 seconds, one email field.
• In-product: ask Claude "register me for Raven updates at you@work.com" — Claude calls raven_register and you're in.

No marketing, unsubscribe anytime. Powered by Resend.

Learning loop

Raven keeps a small local-only log of how you use it so you (and Claude) can spot which patterns you build most often and which gaps show up again and again.

• Location: ~/.raven/usage.jsonl (override with RAVEN_USAGE_LOG=/path).
• What's written: tool name, timestamp, elapsed ms, and a tiny insight object — audit score/warning rule names, pattern type, brand company name, search layer. Never the HTML you audit, never prompt text, never brand copy.
• What's never written: raw page bodies, client content, your work product.
• Disable entirely: RAVEN_NO_USAGE_LOG=1.
• Reflect: ask Claude "what have I been using Raven for?" and it will call raven_reflect, which reads the log locally and summarizes the last N days — most-used tools, recurring audit warnings (likely knowledge gaps), patterns you request most, design systems you reach for.

Nothing is sent to a remote server. If a recurring gap is worth turning into a new Raven principle or pattern, you file an issue by hand — the automated pipeline at github.com/rhinocap/raven-mcp handles it from there.

Development

npm run dev    # Run with tsx (hot reload)
npm run build  # Compile TypeScript
npm start      # Run compiled output

License & attribution

Raven MCP is released under the MIT License — Copyright (c) 2026 Andrew Cunliffe.

If you fork, embed, or redistribute Raven (in whole or in part), retain the MIT license notice and the LICENSE file. If you ship Raven inside another product, include attribution to "Raven MCP — https://ravenmcp.ai" in your acknowledgements.

Raven's knowledge base paraphrases and references work from many third-party sources — Nielsen Norman Group, Laws of UX (CC BY-NC-ND 4.0), Gestalt principles, WCAG (W3C), Mailchimp (CC BY-NC 4.0), GOV.UK (Open Government Licence v3.0), Shopify Polaris, Atlassian Design, and others. Each entry carries a sources URL field. See NOTICE for the full list of upstream sources and license terms; some carry their own conditions beyond MIT.

This is a personal project. It is not endorsed by Intuit Inc. or any other company referenced in its source data.

Data structure

All knowledge lives in src/data/ as static JSON files:

src/data/
  principles/      # Nielsen, Laws of UX, Gestalt, accessibility, typography, color, mobile, D4D
  patterns/        # signup, pricing, nav, forms, landing, dashboard, modals, empty/error/loading, CTA, social proof, mobile
  business/        # monetization, retention, onboarding, growth, metrics
  tokens/          # registry.json + systems/ (stripe, linear, vercel, …)
  content/         # voice & tone: Mailchimp, GOV.UK, Shopify Polaris, Atlassian
    systems/       # registry.json + brand-voice JSONs (Mailchimp, GOV.UK, Polaris, Atlassian)
    principles/    # UX-writing principles (clarity, active voice, error anatomy, …)
    patterns/      # copy recipes for errors, empty states, notifications, form validation
  research/        # study protocols + metrics frameworks
    principles/    # research fundamentals (method match, bias, sample size, ethics, triangulation, …)
    methods/       # qualitative, quantitative, usability
    frameworks/    # HEART, AARRR, North Star, conversion funnel, RICE, OKRs
  service-design/  # service-level principles + patterns + frameworks
    principles/    # Stickdorn, Shostack, peak-end, moments of truth, handoff
    patterns/      # service blueprinting, human handoff, signup-as-service, omnichannel, moments of truth
    frameworks/    # GOV.UK Service Standard (14 points)
  brand/           # brand & visual design
    principles/    # logo, gradient, imagery, hierarchy, brand-as-system
    trends/        # 2026-current.json