Catalog›MCP servers›Media, design & games›RavenRRavenDesign intelligence for coding agents: audits, design systems, and a taste profile agents consult. Open site Website Open in the app JSON README (API)Design intelligence for coding agents: audits, design systems, and a taste profile agents consult.Security checkNo warning signs · Last check: 2026-10-28 · JSONSignals are measured facts, not a safety guarantee. Watch this toolREADMELoading documentation…# Raven MCP **Odin's ravens brought back knowledge of the world — Raven brings back design intelligence.** Raven is an MCP server for coding agents. Click any element in the app you have running locally and say what should change — Raven sends the agent the selector, the computed styles, and your design tokens — then audits the result for contrast, tap targets, and typography. > Raven MCP is a personal open-source project by [Andrew Cunliffe](https://ravenmcp.ai). It is **not endorsed by, affiliated with, or supported by Intuit Inc.** or any other company referenced in its source data. See [NOTICE](https://github.com/rhinocap/raven-mcp/blob/HEAD/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, dropdown/select menus, 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: Conversational Product Voice, 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 Local stdio (npx / from source) is the **full product**: **111 tools**, including Grab, the pattern library, and the file-backed Taste Engine. Hosted endpoints are smaller subsets — pick one path and stick to it. | Path | How | Tools | Taste | Grab | |------|-----|-------|-------|------| | Local stdio | `npx -y raven-mcp` (Claude Code, Cursor `mcp.json`, Codex, Desktop mcpb) | **111** | Yes | Yes | | Public remote | `https://mcp.ravenmcp.ai/api/mcp` | **~45** | No | No | | Auth remote | `https://mcp.ravenmcp.ai/api/mcp-user` (OAuth) | Taste + audits (no Grab) | Yes | **No** | ### Claude Code — one command ```bash claude mcp add raven -- npx -y raven-mcp ``` Prefer **one** Raven entry. If both a local `raven` and a `claude.ai` / remote Raven are connected, the agent sees two overlapping toolsets — disable or rename one (e.g. `raven-local` vs `raven-cloud`) so it is obvious which product you are talking to. ### Manual config (Claude Desktop or team `.mcp.json`) ```json { "mcpServers": { "raven": { "command": "npx", "args": ["-y", "raven-mcp"] } } } ``` ### Cursor Same `mcp.json` snippet as above (`~/.cursor/mcp.json` or project `.cursor/mcp.json`) runs the **full local** server (Grab + Taste). Hosted options: - Public: `"url": "https://mcp.ravenmcp.ai/api/mcp"` — ~45 stateless tools; **no Grab, no Taste**. - Authenticated Taste: `"url": "https://mcp.ravenmcp.ai/api/mcp-user"` — OAuth; Taste yes, **Grab still local-only**. ### Codex Add under `mcp_servers` in `config.toml`: ```toml [mcp_servers.raven] command = "npx" args = ["-y", "raven-mcp"] ``` Codex may prompt to approve many Raven tools on first use — that is client approval policy, not a smaller Raven. ### Claude Desktop — one-click extension Prefer not to edit JSON? Download [raven.mcpb](https://ravenmcp.ai/raven.mcpb) and double-click it. Claude Desktop installs Raven automatically — no Node, no terminal. Package version tracks npm. ### From source ```bash git clone https://github.com/rhinocap/raven-mcp.git cd raven-mcp && npm install && npm run build ``` ## Tools | Tool | Description | |------|-------------| | `get_principles` | Get design principles relevant to a UI context | | `get_pattern` | Get proven patterns for a specific UI type | | `get_business_strategy` | Get business/monetization strategies | | `evaluate_design` | Evaluate a design description against principles. Pass base64 PNG screenshots (`before_screenshot`/`after_screenshot`) for a structured before/after pixel diff with `fix_confirmed`, `changed_ratio`, and changed region. Pass `compact: true` to return only scores and violations (drops full principle/pattern bodies) when the full payload is too large. | | `search_knowledge` | Search across all principles, patterns, and strategies | | `get_checklist` | Get a pre-publish checklist for a UI type | | `get_d4d_framework` | Get Design for Delight framework templates | | `list_design_systems` | Browse available design systems | | `get_design_system` | Get tokens for a specific design system | | `compose_system` | Mix tokens from different systems | | `get_brand_system` | Get a full system styled like a well-known brand | | `audit_page` | Audit HTML/CSS against Raven's quality standards — pass `html` for static audit, or `url` to render headless with optional `scroll_settle` (step through reveal gates, then return to top) and `viewport` parameters; `containerMaxWidth` makes container checks token-aware. Also flags inline SVG icons that hardcode a color instead of using `currentColor`/a token. Pass `compact: true` to return only scores, violations, and fix_priority (drops embedded base64 screenshots) when the full payload is too large. | | `score_page` | Return a per-category (0–10) design score for a page — typography, accessibility, spacing, color, responsive layout, design tokens, structure — derived from the same checks as `audit_page`, plus the overall score/grade, the weakest category, and categories Raven does not mechanically assess (brand, conversion, motion). URL mode also counts determinate contrast failures while keeping indeterminate rows out of numeric scoring. Pass `html` and/or `url` (url capture is local/stdio only; remote rejects `url`) | | `audit_layout` | Evaluate visual rhythm, alignment, and optical balance; detects orphan-stretch (a lonely last-row grid/flex card stretching far wider than siblings) | | `audit_responsive_visibility` | Render a URL at multiple breakpoints and flag content elements that are visible on desktop but hidden on mobile (display:none/opacity:0/zero-size) — categorises each as likely-oversight (content vanishing on mobile) vs intentional (decorative) | | `audit_contrast` | Compute WCAG contrast for rendered text with tri-state `status` (`pass`, `fail`, `indeterminate`), effective backdrops, ratio ranges, and delta-to-pass only where the backdrop is determinate | | `suggest_contrast_fix` | Given failing WCAG color pairs, return the minimal fg/bg change that clears the AA/AAA target — concrete passing values to fix `audit_contrast` failures | | `audit_url` | Render a live URL at each viewport×theme, scroll-settle, fire interactions, capture real pixels + DOM, then run the page/contrast/responsive/blank-media checks **plus** sliced-image edge-symmetry and hover-state white-wash detection over the captures — every finding tagged confirmed/likely-artifact/inconclusive, ranked by severity. Pass `compact: true` to return only findings and summary (drops per-capture base64 screenshots) when the full payload is too large. | | `audit_content` | Per-item content verdicts (pass/warn/fail) for headings, prose, CTAs, labels, captions, metrics & outcomes against UX-writing principles + deterministic heuristics (metric needs number+unit; CTA action-led ≤4 words; prose flags passive/jargon/hedging; caption-vs-heading duplication) — with a before→after rewrite suggestion per item. Pure offline | | `audit_typography` | Typographic-**scale** report over rendered DOM text nodes (or a supplied snapshot) — detects the dominant modular-scale ratio and flags off-scale sizes, checks line-height consistency vs the body rhythm, and flags weight ladders >4 weights or non-standard values. Goes beyond `audit_page`'s pass/fail typography checks | | `audit_tap_targets` | WCAG 2.5.5 / Apple 44pt **web** tap-target audit — enumerates every interactive element (rendered URL or snapshot) and emits a per-element fix table: selector, role, text, measured w/h, per-axis pixel deficit, and a concrete CSS fix, sorted worst-first | | `audit_device_frame` | Flag cropped content in device-mockup frames — `frames` (container box + intrinsic media + object-fit, or a DevTools snippet) detects object-fit:cover crop loss when frame AR ≠ media AR; `clips` (first/last frame PNGs) detects baked-in pan/zoom (Ken Burns); `edge_frames` (PNGs) flags content truncated at a frame edge | | `audit_video_playback` | Render a page and observe whether each `<video>` actually advances — samples currentTime, readyState, error codes, and autoplay-block state, then classifies each clip into playing | paused | stalled | empty | error with evidence. Catches black/non-playing videos that static frame-capture audits miss. Pass `url` to render and observe, or `dom_snapshot` for deterministic offline classification | | `audit_consistency` | Corpus/multi-page audit — compares ≥2 pages and flags cross-page divergence in content-container width and hero heading tier, inferring the canonical (modal) value from the corpus when no token is supplied — catching relational defects that single-page audits miss | | `audit_swiftui` | Audit SwiftUI source against Apple HIG — Dynamic Type, semantic colors, 44pt targets, 4/8pt spacing, AccentColor | | `audit_ios_screen` | Score a rendered iOS screen from an accessibility/view-hierarchy snapshot — 44pt targets + contrast + rhythm, in points | | `audit_ios_privacy` | Audit Info.plist (or Expo app.json) /PRIVACY.md/entitlements/source — usage-string honesty, ATS, Android permissions, bundled secrets, undisclosed default data-egress | | `audit_rn` | Audit React Native / Expo source — touchable a11y labels, 44/48pt+hitSlop targets, font scaling, SafeAreaView, dark mode, against iOS HIG + Android Material | | `generate_design_system` | Generate a custom design system from a brand color | | `list_content_systems` | Browse brand voice & tone systems (Conversational Product Voice, GOV.UK, Shopify Polaris, Atlassian) | | `get_content_system` | Get a brand's voice attributes, tone shifts, vocabulary, grammar, and content patterns | | `get_content_principles` | Get UX-writing principles — clarity, active voice, error anatomy, inclusive language | | `get_content_pattern` | Get copy recipes for error messages, empty-state copy, notifications, form validation | | `get_research_method` | Get qualitative, quantitative, or usability research methods with protocols and checklists | | `get_metrics_framework` | Get a product-metrics framework — HEART, AARRR, North Star, conversion funnel, RICE, OKRs | | `get_service_pattern` | Get a service design pattern — blueprinting, human handoff, signup-as-service, omnichannel, moments of truth | | `get_service_standard` | Get the GOV.UK Service Standard — 14 points for evaluating service quality | | `generate_service_blueprint` | Render a service blueprint as HTML — current state, or current vs. ideal side-by-side | | `get_brand_principles` | Get brand/visual principles — logo, gradient, imagery, hierarchy, brand-as-system | | `get_brand_trends` | Get current (2026) brand and visual-design trends with usage guidance | | `list_creative_models` | Browse provider-agnostic creative model slots for image, video, 3D, audio, character consistency, and analysis | | `list_creative_presets` | Browse creative presets: product photoshoot, marketplace cards, UGC ads, TV spots, social packs, storyboards, infographics | | `create_brand_profile` | Create or update a local brand profile for brand-aware creative jobs | | `get_brand_profile` | Read a local creative brand profile | | `list_brand_profiles` | List local creative brand profiles | | `register_creative_asset` | Register a local path or URL as a creative asset reference — no file bytes are uploaded by Raven | | `create_character_profile` | Create a local character/identity reference profile from registered assets | | `create_generation_job` | Create a provider-agnostic image, video, audio, 3D, campaign, or analysis job payload; optionally execute via `RAVEN_CREATIVE_RUNNER` | | `get_generation_job` | Read a creative generation job and its provider payload/output state | | `list_generation_jobs` | List local creative generation jobs | | `plan_creative_campaign` | Plan a multi-asset campaign and optionally create draft generation jobs | | `score_creative` | Score a prompt/script/concept for hook, benefit clarity, product signal, CTA, channel fit, audience fit, and brand fit | | `create_taste_profile` | Create a named taste profile — a portable design-judgment ruleset (rule_id, clause, category, severity, negative prompt, owner) + precedent corpus, from explicit rules and/or a DESIGN.md-style markdown doc — persisted locally under `~/.raven/taste/` (`RAVEN_TASTE_HOME` override) | | `get_taste_profile` | Load a stored taste profile's full rule catalog, precedent corpus, and surface bindings | | `list_taste_profiles` | List locally stored taste profiles with rule/corpus counts | | `label_finding` | Append a human accept/revise/reject precedent to a profile's corpus — the growth loop; append-only, and accept-verdicts suppress that pattern in future audits | | `get_taste_interview` | Calibration interview, two modes. `kickoff` (default, for a NEW project): a deterministic interview built from the profile's voice rules and eleven design dimensions (typography, spacing, color, layout, motion, imagery, entrance/hero animation, loading states, navigation pattern, aesthetic family, specialty libraries — with Next.js suggested as the default build target for sites) — most questions carry plain-language multiple-choice options, the voice question renders the same message in three registers so you pick by ear, a references question invites example URLs/screenshots to be interviewed about, and an open-ended closer captures signature touches (suggesting the ones you chose on other surfaces once it knows them). Every question is skippable (only identity is required). `refine` (for an ALREADY-bound project you're unhappy with): re-interviews against the stored binding — what fell short, keep/tighten/replace each stored note, voice, optional reject precedent. Answers persist via `bind_taste_surface` | | `bind_taste_surface` | Persist a project's surface calibration — surface string, URL hosts, per-rule severity overrides (incl. `off`), voice note, references — auto-applied by `audit_taste` via `project` or a bound url host. Upserts by project; on a re-bind, omitted fields carry forward from the stored binding (reported as `carried_forward`), while explicit empty values clear them | | `record_taste_decision` | The learning loop — record a taste/direction/design decision the moment it's made during real work (what was chosen, what was rejected, why, and whether the user directed, approved, or corrected it). Recorded decisions evolve future kickoff interviews: recurring choices return as suggested defaults on their dimension's question, and decision categories no standard question covers become new interview questions | | `list_taste_decisions` | The decision ledger, filterable by project or dimension | | `audit_taste` | Judge HTML, copy text, or a live URL against a taste profile — deterministic detectors for gradients, glow/neon, second accent hue, and banned words; pass `source_text` to verify a content port's visible text verbatim with a deterministic word diff; `owner: raven` rules route through Raven's existing page/contrast/tap-target engines; every finding cites a rule_id + concrete evidence (undetectable clauses are reported as `not_assessed`, never guessed); scope-tagged rules activate per `surface` (skipped elsewhere, warn-only when surface is omitted); pass `project` to apply a saved surface binding automatically; `document_kind:'portrait'` skips note-fidelity for documents *about* a surface (rules still run); `data-taste-quote` regions are exempt from detectors so a page is never convicted for quoting the law; verdict BLOCK / WARN / PASS | | `generate_taste_portrait` | Render a bound taste surface as a self-contained designed HTML page (its rules, notes, voice, decisions, and wrong→right corpus) that obeys the surface it describes — art direction routes by the surface's own color permissions; sparse surfaces degrade gracefully. Omit `project` to render every binding plus a gallery. Every portrait passes `audit_taste` (`document_kind:'portrait'`) against its own surface | | `raven_reflect` | Summarize your local Raven usage log to find patterns + gaps | ## Decision Graph The local Decision Graph keeps three node kinds: decisions, evidence, and sources. Five edge types connect them: `supersedes`, `scoped_alongside`, `supports`, `contradicts`, and `derived_from`. Decision status is candidate, active, superseded, or contested; nodes are not hard-deleted. - `decision_add` — add an active decision with its scope, component, rationale, and rejected alternatives. - `decision_evidence` — attach quantitative or qualitative evidence to a decision. - `decision_get` — return one node, its connected neighbors, and attached evidence. - `decision_list` — list active, superseded, contested, or candidate decisions. Candidates are excluded unless `include_candidates:true` or `status:"candidate"` is passed. - `decision_draft` — capture a decision before its rationale is confirmed. - `decision_commit` — confirm a rationale and surface similar active decisions for review. - `decision_supersede` — replace a decision while keeping both nodes and their lineage. - `decision_scope` — narrow two active decisions so they can coexist. - `decision_history` — return a supersession lineage from oldest to newest. - `ingest_transcript` — store a Source node and return the extraction prompt for the calling model. - `decision_import` — read local git history and matching decision documents, then return source-bound extraction prompts. - `ingest_transcript_results` — turn extracted JSON into candidate decisions linked with `derived_from` edges. - `gap_scan` — rank uncovered components, missing or thin rationales, contested decisions, and derived staleness; `digest_only:true` is quiet when no action is needed. For a cold start: call `decision_import` → run the returned extraction prompts with a model → pass each result to `ingest_transcript_results` → review the candidates → call `decision_commit` for each decision to keep. Candidates remain available through `decision_get`, but default `decision_list` and `gap_scan` ignore them until commit changes their status to `active`. Figma comment archives (Markdown files under `figma-comments-archive/` whose first line is `# Figma comments archive: <label>`, with `## Thread <n>` headings) are picked up by default. Their settled threads use thread-aware extraction with `path#Thread <n>` provenance; imported candidates still require `decision_commit` and are never auto-committed. Imported provenance is checked against its Source node before evidence is attached. Git references must be a full or unique-prefix match for a commit included by that import. Document references must match the imported path, optionally followed by a line (`#L12`) or heading fragment. Rejected references are returned in `rejected_source_refs`; the candidate remains available without an evidence node. For transcripts: call `ingest_transcript` → run its extraction prompt → pass the result to `ingest_transcript_results` → review and commit the candidates. Resolve active conflicts with `decision_supersede` or `decision_scope`, inspect lineage with `decision_history`, and use `gap_scan` for health checks. Evidence nodes and `supports` / `contradicts` edges capture quantitative and qualitative results linked to decisions. ### `review_diff` severity policy `review_diff` is advisory by default (verdict caps at `warn`). Two independent, combinable opt-ins escalate matching violations to `error`, producing a failing CI verdict: - `fail_on` — a rule allowlist. Valid rules: `important`, `bare-hex-color`, `hardcoded-font-size`, `hardcoded-font-family`, `hardcoded-spacing`. Start with `important`; add token rules once DESIGN.md tokens are mature. `important` findings can include intentional uses (email-client compatibility, responsive overrides), so expect to justify or restructure those hunks; token rules only fire when DESIGN.md defines tokens (`checks_skipped` tells you when they didn't run). - `fail_on_governed` — escalates findings a recorded decision governs (lexical scope+category association, not a verified contradiction). Opt in as a team strict-mode signal. Escalation is diff-scoped: only newly added lines can fail — existing violations don't block until a diff touches them. The applied policy is echoed back under `severity_policy`. Omitting both keeps the existing advisory behavior unchanged. `review_diff` is local-stdio only (not on the hosted remote endpoints), so wire the policy into CI via `npx raven-mcp`. ## Archive Figma comments Archive your Figma comment history to durable JSON/Markdown before you lose access: `FIGMA_TOKEN=<pat> node scripts/figma-comments-archive.mjs --md <fileKey>` The PAT needs `file_comments:read`. Add `--resolve-nodes` for best-effort node names; it also needs `file_content:read`, and archival still succeeds if resolution is unavailable. Without credentials: in Figma, first show resolved comments and clear any comment filters (hidden threads won't be in what you copy — and they're unrecoverable after cancellation). Figma has no bulk "copy all comments", so select and copy the thread text from the comments panel, then run (macOS): `pbpaste | node scripts/figma-comments-archive.mjs --paste design-review` (the last word is your archive label — any name without spaces; add `--out somedir` to choose the folder). Or run the command bare and paste into the terminal, ending with Ctrl-D. Separate threads with a blank line; within a thread, an author line followed by a timestamp line ("2 days ago", "Yesterday", "Mar 4, 2026") starts each comment. Paste mode writes `<label>.txt` (your paste, byte-verbatim — the durable record) and always renders the readable `<label>.md` archive. Skim the `.md` against your paste: message lines that themselves look like a timestamp, or blank lines inside one comment, can shift how the `.md` groups things — the `.txt` is always exact. An existing label is never overwritten; pass `--force` to replace it. ## Click-to-change (grab) + DESIGN.md **Grab is local-stdio only.** Hosted Cursor/Claude remote endpoints do not expose Grab — click-to-change needs a loopback bridge on your machine. Use local `npx` / Cursor local `mcp.json` when you need Grab. Raven Grab connects a local page to your agent so you can click an element, describe the change, and send its selector, computed styles, matching DESIGN.md tokens, and token choices back to the session. The bridge runs on loopback and the returned script tag carries the capability key required by its routes. Computed styles are editable inline, and edits are sent to the agent as `styleEdits`. Setup takes under a minute: 1. Start your local dev server. 2. Call `start_grab_session` with `proxy_target` set to the local server URL. `path` to a `DESIGN.md` is optional when `proxy_target` is set (Raven creates a minimal temp DESIGN.md); required when you only inject the script without a proxy. 3. Open the returned bridge URL. The overlay is already included on HTML pages served through it. 4. Click elements and enter the changes you want in the Grab panel. 5. Call `get_grabbed_elements` to receive the queued selections and instructions (draining frees queue capacity for later sends). For a page you control, you can omit `proxy_target` and paste the returned `<script>` tag into the page instead. Use `read_design_md` to inspect a DESIGN.md file and its flattened token index, `init_design_md` to create one from a stored Raven system, a blank template, or a [getdesign.md](https://getdesign.md) starter, and `update_design_md` to set, rename, or remove one token without rewriting the rest of the file. ### Pattern library — keep what you grab, then translate it `proxy_target` also accepts a third-party URL, so you can grab from any site you are allowed to view, not just your own dev server. What you grab is otherwise gone when the tab closes, and it arrives as another site's literal values. Four tools close that loop: - `capture_reference` — persist a grabbed selection under `~/.raven/references`: selector, computed styles, hover/focus states, bounding rect, truncated HTML, your own note, and tags. One JSON record per capture, so grabbing the same element twice keeps both. It also renders the captured element back into a PNG beside the record, because nobody can pick a pattern out of a style map. That render is **offline** — every external request is aborted, so a stored reference never reaches back out to the site it came from — and it runs with **scripting disabled**, so a script in a captured element cannot execute. The record says so: `fidelity: "offline"`. - `search_references` — find it again later by free text, host, owner, or tags, with a per-result score and a `why` naming the fields that matched. Each result carries a `display` object holding the credit line *and* the image path together, so a consumer reaching for the picture carries the attribution out with it. **Looking is not copying**: a result reports `html_available` but omits the captured markup, because browsing a corpus of other people's work should not hand back their markup as a side effect of looking at it. Pass `include_html: true` when you actually mean to read the structure — the response then names whose markup it contains. Everything a browse is *for* is in the default result: the picture, the selector, the rect and the computed styles. - `map_reference_to_tokens` — translate the captured literals onto **your** DESIGN.md tokens, so the code an agent writes uses your type ramp and palette instead of pasted values. Every binding carries the resolved **value** and CSS variable alongside the token name — a name alone is not something you can write into a stylesheet — and an aliased token resolves to the literal at the end of its `$ref` chain. Pure and deterministic: no model, no network. It reads the stored styles directly, so the whole show-it-then-translate-it path runs without the markup ever leaving Raven. - `forget_references` — remove a single reference by `ref_id`, or every reference from a host (subdomains included). Takes the PNGs with it. Destructive and permanent, so the host sweep refuses to run without `confirm: true` and tells you how many records that would remove first. How the mapping decides, because a wrong binding is worse than a stated gap: - **Colour** matches on RGBA distance, not RGB — the same hex at a different opacity is a near miss, not an exact hit. Hex, `rgb()`/`rgba()` in both comma and space form, `hsl()`, and the CSS named colours all resolve; a syntax the matcher cannot read (`oklch()`, `lab()`) says so by name instead of reporting your palette as empty. - **Lengths** normalize to px at a 16px root. Percentages and viewport units need a containing size and are returned as gaps with that reason, never converted on a guess. - **Family before proximity.** A property that belongs to a token family binds inside it: `padding-top` takes a spacing token even when a type token is numerically closer, and `line-height` takes the leading token over an equally-exact size token. When no token in the right family is close enough, the result is a gap that *names* the cross-family near miss ("the closest token by value is `space.4` (16px), but it belongs to a different family") rather than binding `font-size` to your spacing ramp. - Ties break on distance, then family fit, then shortest and lexicographically-first token path, so the same inputs always produce the same binding regardless of token order. - Broken `$ref` chains and cycles in your DESIGN.md come back in `diagnostics` even when every property still found a match — a defect in your own token file is reported, not swallowed. Respect the source. Grab from sites you are permitted to access; the tools never bypass a paywall or a login, and `owner: "third-party"` is recorded on every capture. **Attribution and takedown.** Every third-party record keeps the URL, host, app name and capture date it came from, and `search_references` derives a credit line from them on read — so the credit cannot go stale, and it travels with the picture rather than beside it. Raven claims no ownership of anything you capture. Your corpus is **local**: it lives in `~/.raven/references` on your own machine, and this project hosts no copy of it. So a takedown is something you run, not something you request — if a rights holder asks you to remove their material, `forget_references` with their host removes every record from that host and every subdomain, and the images with it: ``` forget_references({ host: "example.com", confirm: true }) ``` It reports what it removed, what it could not read, and anything it tried to remove and failed — those are three different answers and it does not collapse them into one. A removal that fails part-way leaves the record in place rather than the picture, so running it again finds and finishes what was left. The confirmation prompt names the exact records it would take, and passing those ids back as `expected_ref_ids` pins the removal to them — anything captured in between is reported rather than swept up. If you believe this project itself is distributing your material, open an issue at <https://github.com/rhinocap/raven-mcp/issues>. One boundary worth stating plainly: while the bridge is proxying a third-party site, that page is served from the bridge's own origin, so scripts on it are same-origin with the Raven overlay and can read your DESIGN.md token names and values. Raven withholds the DESIGN.md file path and every authoring route (layer moves, template and component writes, batch commits) for the duration of a proxy session, but proxy sites you would be comfortable showing your token list to. ## 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 44×44**pt** 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 for every text element, reporting a tri-state `status`: `pass`, `fail`, or `indeterminate`. Determinate rows include `ratio`, `aa`, `aaa`, and `delta_to_aa`; indeterminate rows keep `required_aa` but publish those four metrics as `null`. Gradient and layered backgrounds expose `effective_bg` plus `ratio_min` / `ratio_max` when a trustworthy candidate range exists, and results summarize `indeterminate_bg_rows` / `indeterminate_bg_count` separately from AA failures. Real-backdrop compositing applies to **URL mode**. Raven walks the rendered DOM ancestor chain, composites parseable colors and gradient layers in CSS paint order, samples gradient interiors, and normalizes modern computed colors through the browser canvas. This intentionally stops at the DOM-ancestor ceiling: opacity, `display:contents`, positioned transparent chains, photos, unsupported layers, and cross-stacking-context sibling backdrops are reported indeterminate; Raven does not pixel-sample across stacking contexts. Snapshot mode retains the pre-existing supplied-`bgColor` / over-white model and announces that scope in `mode_note`. **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 rows with status, determinate failures with delta-to-pass, effective background evidence/ranges, and separate indeterminate summaries. `suggest_contrast_fix` accepts only determinate failing rows; indeterminate or null-ratio evidence is skipped rather than converted into a fake color recommendation. **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 with a short pause at each step so `IntersectionObserver` / whileInView thresholds can fire, recomputing page height as lazy content appears and requiring consecutive stable-height observations at the bottom. Raven then waits for finite animations triggered anywhere in the walked page before returning to the top and re-settling for capture. Smooth scrolling is temporarily neutralized. The whole walk/settle is capped at 4s; if a very long, continuously growing, or still-animating page cannot fully settle, Raven emits a capture warning rather than silently trusting the result, and `animationsSettled` remains false. Unloaded videos (preload="none") are played to detect if they render blank. - **Entrance-animation settle (always on)** — before extracting content or screenshotting, Raven polls `document.getAnimations()` until no finite animation in or near the viewport is still running (infinite spinners/loops are ignored), capped at 3s by default. Library callers can set `animation_settle_timeout_ms` (hard maximum 10s). Pages whose heroes enter via `animation-delay` + `backwards` fill are captured settled, not blank or mid-flight; `animationsSettled` in the capture metadata reports whether quiescence was reached. - **False-blank detection (always on)** — at capture time Raven measures text-bearing leaf nodes and media/content elements across the captured page. If more than 30% are effectively invisible through `opacity:0`, `visibility:hidden`, or a fully transparent text color, `capture_warnings` includes `reveal-gate-false-blank: …` so capture-backed audit callers know the rendered audit may be untrustworthy. - `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. Close the token-polish loop against a real git worktree: ```bash npx raven-polish --apply --verify "npm test" ``` The CLI is dry-run by default (or `node scripts/raven-polish.mjs` in this repo); it exits 1 when polish is proposed or any finding has severity `error`, and exits 0 only when there is nothing to polish and no errors. Pass `--range main...HEAD` to review committed work. It checks a proposed patch before applying it, then runs `review_diff` again on the real repository state. For CI, copy [the example workflow](https://github.com/rhinocap/raven-mcp/blob/HEAD/.github/workflows/examples/raven-polish.yml) into `.github/workflows/`. ## 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](https://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. ### After you upgrade Claude Code snapshots the tool list when the MCP server connects. After upgrading, restart the session or use `/mcp` to reconnect and see new tools. Claude Desktop snapshots the tool list when the MCP server connects. After upgrading, restart the app to see new tools. Codex CLI also requires a per-tool `approval_mode` entry in `~/.codex/config.toml`; without it, calls to new tools are cancelled. Run `node scripts/sync-codex-approvals.mjs` to see what is missing, then add `--write` to append the entries. The appended entries auto-approve those tools, so review the printed list; newer Codex versions can alternatively set a server-level `default_tools_approval_mode`. ## Start every project calibrated Taste is per-surface: the same designer wants monochrome one-accent rules enforced on their portfolio and *none* of them on a product site, with a slightly different voice on each. The Taste Engine handles this with a **kickoff interview** (once per project — every question skippable, most with plain-language multiple-choice options, from navigation pattern to aesthetic family to specialty libraries) whose answers persist as a surface binding that every future audit applies automatically. And when generated work misses, `mode:'refine'` turns that dissatisfaction into a re-interview against the stored binding instead of a dead end. Starting a brand from *nothing* — no product, no palette, just an idea? The same interview is step one of a full genesis flow (invent the brand in conversation, generate assets with your own image tools, approve a mood board, land a design system in `DESIGN.md`): [docs/brand-genesis-flow.md](https://github.com/rhinocap/raven-mcp/blob/HEAD/docs/brand-genesis-flow.md). Raven ships this flow in its MCP server instructions, so agents that honor server instructions (Claude Code, Claude Desktop) run the interview at project kickoff on their own: `get_taste_interview` → ask the user → `bind_taste_surface` → done. If your client doesn't surface server instructions — or you want the ritual to be non-negotiable — add one line to the project's `CLAUDE.md` / `AGENTS.md`: ```markdown Before the first design/UI/copy work in this repo, run Raven's get_taste_interview (profile <name>, project <repo-name>); if existing_binding is null, ask me its questions and persist with bind_taste_surface. Pass project:'<repo-name>' on every audit_taste after that. ``` Already-calibrated projects cost one cheap call (`existing_binding` comes back non-null and the agent proceeds). Uncalibrated audits still work — scoped rules just demote to warn and the result carries a `calibration_hint` — so calibration is never a wall, only a sharpener. ## 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](https://github.com/rhinocap/raven-mcp) handles it from there. ## Your data on the hosted server Raven also runs a hosted remote MCP server with two endpoints. The anonymous endpoint is stateless — no store is ever attached to it, so nothing you send is written anywhere. The authenticated endpoint (used for the Taste Engine's cross-session profiles, surface bindings, and decisions) keys everything to your account and stores it in Upstash Redis, namespaced under your verified user id; your bearer token itself is never persisted. You can erase all of it at any time with the `delete_taste_data` tool (`confirm: "DELETE"`) — it removes every key under your namespace and confirms nothing remains. Full details, including the exact key layout and the rate-limit counters that aren't part of your data, are in [docs/remote-mcp-privacy.md](https://github.com/rhinocap/raven-mcp/blob/HEAD/docs/remote-mcp-privacy.md). ## Development ```bash 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 [Apache License, Version 2.0](https://github.com/rhinocap/raven-mcp/blob/HEAD/LICENSE) — Copyright (c) 2026 Andrew Cunliffe. If you fork, embed, or redistribute Raven (in whole or in part), retain the Apache-2.0 license notice, the `LICENSE` file, and the `NOTICE` file. If you ship Raven inside another product, include attribution to "Raven MCP — https://ravenmcp.ai" in your acknowledgements. Raven's knowledge base references work from many third-party sources — Nielsen Norman Group, primary academic literature for the UX/psychology laws (Fitts, Hick, Miller, and others), Gestalt principles, WCAG (W3C), plainlanguage.gov (public domain), GOV.UK (Open Government Licence v3.0), Shopify Polaris, Atlassian Design, and others. Each entry carries a `sources` URL field. See [NOTICE](https://github.com/rhinocap/raven-mcp/blob/HEAD/NOTICE) for the full list of upstream sources and license terms; some carry their own conditions beyond Apache-2.0. 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: Conversational Product Voice, GOV.UK, Shopify Polaris, Atlassian systems/ # registry.json + brand-voice JSONs (conversational-product-voice, 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 ``` DetailsTypeMCP serversTopicMedia, design & gamesPublisherai.ravenmcpSourceofficialCategoryToolsWhere it runsOnlineMCP endpointhttps://mcp.ravenmcp.ai/api/mcpVersion2.6.0Stars7Proposed changes1Last update2026-09-24T21:06:53ZProject statusActiveLanguageJavaScriptLicenseApache-2.0Added2026-08-29 03:00:36Updated2026-08-29 03:00:36Source idai.ravenmcp/raven-mcpMoreMore mcp servers on Media, design & gamesAll from ai.ravenmcpA–Z index
