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524,648 tools. Updated 2026-09-06 16:29

"Ant Design" matching MCP tools:

  • Score a URL for design-system AI readiness — the 6th maturity axis (zeroheight 2026). 10 checks probe the target origin for machine-readable artifacts: DTCG token files, llms.txt, agent.json, MCP endpoint (tools/list), DESIGN.md, token $description, component schemas, sitemap.xml, robots.txt, and Open Graph/Twitter meta. Use this to verify whether a design system is the default context AI tools build from, or whether AI is silently working around it. When NOT to use: for full design-contract scoring, use designesy_score; for AI-drift detection, use designesy_drift_score. Executable — fetches the URL and probes the origin via HEAD/GET for each artifact. No browser needed. Returns JSON: { ok, url, score (0-100), grade (A-F), pass, warn, fail, total, checks[{id, item, category, status, detail}] }. Results cached ~24h per URL.
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  • Use when the user asks how a public board is wired, what a component (e.g. U1) connects to, or which pins are on a net (e.g. GND), in that design. Returns the board's latest geometry-free connectivity. No focus returns a bounded overview (components + a net index); ref returns one component and the nets it connects to with the other pins on those nets; net returns the pins on that net. These are in-design connections, not an authoritative manufacturer pinout, and a very large design may be truncated (the response flags this). Prefer a focused ref or net over repeated overviews. Use get_bom for purchasing and read_file for raw source. If nets are still computing, continue with the components shown and try again shortly rather than inferring connectivity.
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  • The WHOLE design as one readable document — Vision (+ Project DNA) → every System spec → reference notes, compiled deterministically from the current design. Read this to understand a project end-to-end instead of walking list_systems → get_system N times. Returns markdown plus the project `version` it was compiled from. Long designs come back PAGED — the header says 'part N of M', call again with `page: N+1` for the rest. Pass `for_summary: true` to get the condensed projection instead (every system's Goal + Boundary, tables stripped, one page) — that is what you should summarize from.
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  • Get the Designesy Design Review framework — an 8-dimension rubric (Purpose, Clarity, Context, Inclusion, System coherence, Durability, Delight, Responsibility) plus the agent prompt, output format, and verification checklist for a qualitative design critique. Use this when you want a structured rubric to critique a design holistically, rather than a numeric compliance score. When NOT to use: for a deterministic numeric score, use designesy_score; this tool gives you a rubric, not a number. Read-only — returns the rubric + prompt. The calling agent performs the actual critique (this tool does not evaluate the design for you). Returns JSON: { rubric, dimensions[8], agent_prompt, output_format, verification_checklist }. Pass artifact/purpose/context/rules to get a pre-filled critique prompt; omit all four to get the blank framework.
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  • Diff two design systems from live URLs — the only URL-scoped design-token diff engine. Fetches both URLs in parallel, extracts their :root custom properties, and produces a structured diff across 8 dimensions: tokens added (in A not B), removed (in B not A), renamed (heuristic Levenshtein ≤ 2), value-changed (same name, different value), scale-stop-changed (spacing/radius/color scale steps), contrast-drift-per-pair (WCAG contrast ratio change for shared color tokens), structure-delta (token count + category distribution), and score-delta (runs /score on both URLs and diffs). Use this to answer "what actually changed between two design systems" or "how does our design system differ from a reference". When NOT to use: for single-site drift detection, use designesy_drift_score; for continuous monitoring, use designesy_monitor_score. Executable — fetches both URLs, extracts CSS + tokens, computes diff. No browser needed. Returns JSON: { ok, urlA, urlB, score (0-100, diff completeness), grade, pass, warn, fail, total, tokensA, tokensB, added[], removed[], renamed[], valueChanged[], scaleDiff, structureDelta, contrastDrift[], scoreDelta, checks[] }. Results cached ~24h per URL pair.
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  • Rewrite Tailwind CSS classes into their canonical short form. Turns arbitrary values back into real design tokens: p-[1rem] becomes p-4, rounded-[24px] becomes rounded-3xl, -bottom-[4px] becomes -bottom-1. Backed by 12,438 replacements generated from Tailwind 4.3.3's own design system, so the answers are authoritative rather than guessed. Handles one class at a time and does NOT merge across classes (px-4 py-4 into p-4, w-6 h-6 into size-6) — that needs the surrounding group to be safe to collapse; use NormWind for that.
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  • Measured design CHANGE HISTORY for a live-decoded domain — the Decode Ledger. Token-level diffs between deep decodes over time: "radius 4px→8px", "primary hover #4032C8→#0A2540", "motion dominant 150ms→200ms", each dated. Use it to see how a product's design system is EVOLVING (no screenshot library can backfill this). site = a domain ("stripe.com") or product name. Returns first/last decode dates, decode_count and the dated change entries; empty history = measured, stable so far.
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  • CONSULT THIS DURING DESIGN — before and while you design a skill/solution. Describe what you're building; it returns POINTERS to the platform capabilities that fit (per-actor storage, widgets, triggers, sub-agents, mobile data, run-scripts, multi-skill, GitHub, …), each with the /spec topic to read next (via ateam_get_spec) and the tool to wire it. Also returns 'missing' hints (capabilities your goal implies but the design hasn't wired) and lifecycle hints (e.g. connect GitHub when the project will iterate). ADVISORY ONLY — you decide and own the design. Stateless: pass the current design_state each call; consult it as often as you like as the design evolves. If the reply carries `truncated: true`, the answer ran past the length budget and was CUT OFF: what is there is correct, but a capability's ABSENCE proves nothing — ask again with a narrower goal, or use ateam_spec_search, before concluding the platform lacks something.
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  • Search Blueprint principles by free-text query and return the closest matches ranked by relevance. Use this to find principles related to a specific design challenge, failure mode, or keyword (e.g. 'reversibility', 'approval flow', 'delegation boundary'). Returns principle title, cluster, definition, rationale, and implementation heuristics. Prefer this over principles.list when you have a specific topic in mind rather than wanting all principles. NOTE: search currently covers the 10 agentic principles only; for the 8 experience-design laws or the 8 spec-quality laws use principles.list(lens='surface') / principles.list(lens='spec') until search spans all three lenses.
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  • Shows HTML content on a display: menus, dashboards, welcome pages, schedules or any custom design. slot 'live' (default) replaces the current content; slot 'idle' stores the default/fallback content shown when nothing live is active (idle requires admin scope). Always pass a short description so later content reads stay meaningful. Exactly one of html or base64_html. For external web pages use send_url; to edit current content call read_display_html first. For polished results load prompt render_premium_display_html or resource agentview://public/design-system. Requires content scope.
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  • Search Blueprint principles by free-text query and return the closest matches ranked by relevance. Use this to find principles related to a specific design challenge, failure mode, or keyword (e.g. 'reversibility', 'approval flow', 'delegation boundary'). Returns principle title, cluster, definition, rationale, and implementation heuristics. Prefer this over principles.list when you have a specific topic in mind rather than wanting all principles. NOTE: search currently covers the 10 agentic principles only; for the 8 experience-design laws or the 8 spec-quality laws use principles.list(lens='surface') / principles.list(lens='spec') until search spans all three lenses.
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  • Write the Design Document's Overview — the human-readable page a new team member reads first. WRITES DIRECTLY (no Inbox): it is a derived, clearly-labelled AI summary, not design truth, and the owner can clear or rewrite it in one click. HARD RULES, same as the in-app button: use ONLY facts stated in the design (call get_design_document with for_summary:true first); invent no mechanics, numbers or names; describe, never evaluate; write in the design's dominant language. Structure: `### What this is` · `### The core loop` · `### How the systems fit` (which system feeds which — the part a raw spec list cannot give) · `### Edges` (ONLY if the design states scope limits / open questions). 250-400 words, no top-level heading. Forge stamps the project version it was compiled from, so the owner is told when the design has moved past it.
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  • Get the Designesy design-system contract — the canonical tokens, motion, acoustic, takt, cadence, typography, components, and verification rules that define what the Designesy org considers legitimate design. Use this when you need the actual contract values (token names and values, motion timings, accessibility rules) to author, check, or bind a design. When NOT to use: for a pass/fail score of a live site, use designesy_score; for an agent-skill-format export, use designesy_skill_md. Read-only — cached ~24h server-side. Returns the full contract JSON, or a single section when "section" is provided. Pass section to get one slice (e.g. "motion" for just the motion tokens) instead of the full contract — saves tokens when you only need one dimension.
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  • What actually changed in the web's design systems lately — the nightly Drift Ledger feed. Mozaika re-measures ~100 of the most-referenced products every night and records a dated row per product even when nothing moved, so this is a real time series, not a guess: how many products held every token, which ones shipped a change that stuck (with before/after values and the date), and which design tokens move most often across the web. Use it to answer "does anyone actually redesign?", to ground a claim about design churn with a citable measurement, or to spot that a reference you rely on has moved. For one product, call get_design_drift(domain). Args: limit: how many confirmed changes to return (1-40, default 10). Free.
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  • Store a Roastify design JSON in your library (a commit in your GitHub repo). The browser courier reads a saved product's design and calls this to shuttle it up. On the way in, the design's fonts[] is REPAIRED — Roastify's own schema migration leaves a lossy fonts[] (a dropped family, a bad weight), so a stashed design would otherwise carry that damage; the repair rebuilds fonts[] from the families the text actually uses so it renders in its intended fonts. Only the load list changes; the text and its fonts are untouched. Inline images are de-duplicated. This does NOT touch Roastify.
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  • Get ranked, purchasable offers (price, ETA, preview image) for fabricating a physical item from a design file. process=fdm_print for 3D printing a model (STL/OBJ/PLY/3MF/AMF/STEP/IGES), process=cnc or process=sheetmetal for machined/bent metal parts (STEP, IGES, DXF), process=decal for stickers/decals from artwork (any common image or design file — PNG/JPG/HEIC/TIFF/GIF/BMP/WEBP/AVIF/SVG/PDF/AI/EPS/PSD/CDR, auto-converted). A .ufp file (UFP part container: the design plus saved spec/constraints in one) is accepted anywhere a design file is — its saved intent applies automatically and anything the user states now wins. If the user just drops a file and asks for a price, omit process — UFP routes it. Provide the design either as design_file (an image/file the user attached or you generated — preferred) or file_url (a public URL). REORDERS: if the user has a UFP part number (from a receipt email or a previous session, looks like UFP-… or part_…), pass it as part_number INSTEAD of any file — the stored design and spec are reused and re-shopped across all current vendors. Locked parts additionally require share_key (from the owner's share link). Returns offers across vendors like Google Flights returns flights.
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  • Returns AdCritter design guidance for an entity at a caller-chosen guidance level - screen experiences, API integration patterns, and design philosophy. The default ('full') returns step-by-step prescription (exact layouts, colors, copy text, column orders). Request 'patterns' for balanced hints including common design patterns with softened vocabulary. Request 'facts' if you have strong visual-design instincts and just want API integration bindings (or call adcritter_get_api_reference and adcritter_get_usage_guide directly and skip this tool). Guidance is format-agnostic - it describes outcomes and integration, never prescribes frameworks or architecture. Available entities: ad, advertiser, audience, authentication, blueprint, campaign, geo, media-asset, plan, report, settings.
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  • Interactive single-site design-conditions explorer. Returns full ASHRAE design conditions + diurnal chart for the requested scenario. In MCP Apps-capable hosts (Claude Desktop, ChatGPT, VS Code, Goose), the response renders as a widget with sliders for SSP / year / percentile / UHI — dragging a slider re-calls this tool live. Use when a user wants to interactively tune a single site. For multi-site comparison, use analyze_weather(urls=[...]) instead. Defaults to present-day TMY (no morph) — pass ssp+year for future scenarios. P75 default percentile is design-realistic; P50 underestimates the tail. No auth required.
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  • Decode a standby.design URL (or raw hash) and return an overview of the full design system: color palette, type scale, spacing & layout, shape tokens, and icons — plus per-tool edit links. Always give the standby.design/system URL to the user — the link is the deliverable.
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  • Work out why a cloning experiment failed: no colonies, every clone empty vector, or no PCR band. Takes your design (method, parts, enzymes, primers, host methylation state) plus what you actually observed (colony counts on the plate and on each control, screening tally, band sizes, whether the ladder ran) and returns causes ranked by evidence — each with the deterministic fact from the design or the observation that implicates it, the cheapest observation that would separate it from the next candidate, and the next experiment. Causes the observations eliminate are reported as eliminated, naming the observation that did it; causes the design makes impossible are not listed. No probability is computed anywhere — the ordering is of evidence, not of likelihood, and `ranking.evidenceBased` says so when the inputs separate nothing.
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  • Crea una escena 3D con uno o varios muebles y devuelve un link editable directo al editor de Mueblito — el link /d/<code> abre el diseño listo para ver en 3D, mover piezas, ajustar medidas y generar presupuesto. Usala cuando el usuario quiere visualizar cómo quedarían los muebles en un espacio o compartir un diseño con un carpintero o cliente. Prerrequisito: si no sabés los ids de los presets, llamá primero a listar_presets. Cada mueble se especifica con preset o params; la posición y rotación son opcionales (si no se dan, los muebles se distribuyen en fila automáticamente). Limitación: el link es estático (no se guarda en servidor); no requiere cuenta. (create 3D design, furniture layout, shareable room design, Argentina interior design)
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