Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Beschreibung vom Entwickler (englisches Original)
A 3D compiler for coding agents: objects, worlds and games as small recipes. Emits Godot and STL.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"mojulo": {
"command": "npx",
"args": [
"-y",
"mojulo"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add mojulo -- npx -y mojuloIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"mojulo": {
"command": "npx",
"args": [
"-y",
"mojulo"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"mojulo": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"mojulo"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 6. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto zombico bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (3.1.1) kam vor 21 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Node.js auf deinem Computer.
In derselben Kategorie: Figma Context MCP, Claude Real Video, Figma MCP Server
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Let any LLM watch a video locally — and search everything it has ever watched.
The Figma MCP server brings Figma design context directly into your AI workflow.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
Mojulo is a 3D factory with a media studio for coding agents: an MCP server that runs wherever your agent runs, on your machine or in the throwaway Linux box it gives itself, where the agent you already run (Claude Code, Codex, any MCP host, or a chat app that can open a Linux box: Claude, ChatGPT with Codex, Grok, Meta Muse, Google AI Studio) builds things to play, print, read, hear and watch (games, objects, walkable worlds, pictures, scores) by conversation, and what gets stored is source, not a mesh. Every artifact is a small deterministic recipe: a few hundred bytes of JSON, or an OpenSCAD program, that a kernel compiles back to the same geometry on every read (byte for byte on the same platform), and that emits to Godot, Blender, Unity, Unreal, glTF, OpenUSD, or print-ready STL / 3MF at true scale. Small enough to carry home from a box and re-mint on your own disk. A compiler, not a generator: you edit and diff the recipe like code, and renders are disposable. No API key, no account, no external telemetry. Your agent does the thinking; mojulo holds the state and does the geometry.
npx mojulo init
Needs Node 22.14+ and an MCP-capable coding agent (Claude Code or Codex; Claude Desktop works too). init finds the hosts on your machine, asks once per host, and opens the dashboard at http://localhost:3001. Everything lands in ~/.mojulo/. Then open a fresh agent session and ask: "what is this?"
In Claude Code you can install the plugin instead: /plugin marketplace add zombico/mojulo, then /plugin install mojulo@mojulo. It starts the same server pinned to one version (plugins/mojulo). The plugin build leaves out the handoff tools for AI image, voice and mesh generators and never downloads a browser, ffmpeg or the search model on its own. Use the plugin or init for Claude Code, not both: two registrations run two servers.
The first install is the big one: npx pulls a ~7 MB package that lands, with its dependencies, at about 227 MB on disk plus about 140 MB of npm cache (about 79 MB downloaded; measured for 3.0.0 on one macOS arm64 machine, where a cold start answered in about 4.5 s, nearly all of it npm's install). The biggest pieces are node-web-audio-api (audio), manifold-3d (exact booleans) and better-sqlite3. The dashboard is a separate package fetched the first time you open it, and the local search model is the opt-in mojulo install recall (semantic_search ranks lexically without it). Nothing in the list reaches the network on its own; the per-dependency sheet is in docs/tech-requirements.md. Verified on macOS (Apple Silicon) and on native Windows under Claude Code (init and a first render). On Linux x64 the test suite runs in CI, and the agent-box path below has been run to a mint and an export from the Claude app and web, ChatGPT work mode with Codex, Grok chat, Meta Muse and Google AI Studio.
Wire it by hand instead
# Claude Code (--scope user makes mojulo available in every project):
claude mcp add --scope user mojulo -- npx -y mojulo
[mcp_servers.mojulo] command = "npx" args = ["-y", "mojulo"]
// Claude Desktop: add under "mcpServers" in claude_desktop_config.json, then restart.
// If it fails with "spawn npx ENOENT", replace "npx" with the absolute path from which npx. "mojulo": { "command": "npx", "args": ["-y", "mojulo"] }
Open the dashboard on its own with npx -y mojulo-ui. It is its own npm package since 3.0.0, so your agent's npx mojulo start never downloads it; npx -y -p mojulo mojulo-ui still works and fetches the matching version from npm on first use. The mojulo bin is also a CLI over the tool registry: npx mojulo tools, npx mojulo help mint_solid, npx mojulo call version.
Mojulo runs wherever your agent runs, and a recipe minted in one place re-mints as the same geometry in the other. Two shapes, one install.
In your agent's box, nothing on your machine. One sentence to the agent installs mojulo in the Linux box it gives itself:
Each of those has been run this way, from the sentence to a mint and an export handed back. You get the same recipes and the same exports, as files: the export result names this host's door, an artifact page, a PR, a file card or Muse's Library. The box has no dashboard you can reach, a scene-to-PNG bake needs a browser it may not be allowed to fetch, and on most hosts it is gone when the session ends, so ask for the bundle (one zip: world.html, mesh, print STL for literal-scale objects, recipe.json, README) and keep the recipe. Meta Muse is the exception: one VM behind all its clients, where ~/.mojulo stays across conversations. A box with no MCP client never sends initialize, so tell the agent to run npx mojulo orient first: it prints what an MCP client is handed at connect, translated to the shell, and points at the routing index. Claude's box built a 47-part phone at true scale this way and handed back the glTF; Grok chat's sandbox minted a city from the shell. Blender installs in those boxes too.
On your machine: macOS, Windows, Linux. npx mojulo init wires mojulo into the agents it finds, opens the dashboard at localhost:3001, keeps everything under ~/.mojulo/, and probes your PATH for the optional local workers (Blender, a slicer, OpenSCAD, the game engines). This is the whole loop, and where a recipe from a box comes home to.
Who has run it where. persistent (your machine, or a box that keeps ~/.mojulo/) · ephemeral (the web agent's throwaway Linux box; keep the recipe). Blank means not verified yet, not "does not work".
| agent | macOS | Windows | Web Agent Linux Box (Headless) |
|---|---|---|---|
| Claude Code | |||
| Claude app (macOS, Windows, web, iOS) | |||
| ChatGPT (work mode; Codex) | Codex | ||
| Hermes Agent | |||
| Grok (Build; chat) | Build | ||
| Meta Muse (iOS, web, macOS app; one session across them) | |||
| Google AI Studio |
Two things are opt-in, and the choice is the same in both places:
| add | with | what you get |
|---|---|---|
| creative (installed by default) | plain npm install | worlds, audio, fonts for wordmarks, exact booleans, OpenSCAD-in-process, sharp for skins and sprite sheets. npm install --omit=optional sheds those helpers (about 115 MB); the studio tools still list and run, and a call that needs a missing helper says so. |
| recall | mojulo install recall | the embedding model behind semantic_search. Without it, search still answers, ranking by the words in your ask (about 480 MB of runtime plus a 130 MB model, kept under ~/.mojulo/ so it survives upgrades). Most sessions never need it: the agent reads the tool index and the vocab cards directly. |
mojulo install with no argument prints which of the two are present.
The chatbot factory is no longer part of mojulo as of 3.0 and is moving to its own project. Earlier 2.x versions that include it are unmaintained and have known security issues (SECURITY.md). mojulo install chatbot now installs nothing and prints this notice.
Upgrading from 2.x? Read Upgrading from 2.x first: an unpinned npx mojulo moves to 3.0 on its next start, and 3.0 re-encrypts saved provider keys so 2.x can no longer read them.
Each one is a sentence to your agent, the tool it reaches for, and the recipe that gets stored. Every example below runs keyless and offline.
The agent calls mint_solid { kind: 'scad' } and the program is the recipe, stored verbatim:
board_w = 85; board_d = 56; clear = 1; wall = 2; floor_t = 2; wall_h = 12;
holes = [[3.5, 3.5], [61.5, 3.5], [3.5, 52.5], [61.5, 52.5]]; // the Pi's M2.5 pattern module rounded_box(w, d, h, r) { hull() for (x = [r, w - r], y = [r, d - r]) translate([x, y, 0]) cylinder(r = r, h = h, $fn = 48); } module tray() { color("#3a3f4b") difference() { rounded_box(board_w + 2 * (clear + wall), board_d + 2 * (clear + wall), floor_t + wall_h, 3); translate([wall, wall, floor_t]) rounded_box(board_w + 2 * clear, board_d + 2 * clear, wall_h + 1, 1.5); translate([board_w + 2 * clear + wall - 1, wall + 2, floor_t + 3]) cube([wall + 2, board_d + 2 * clear - 4, wall_h]); // USB / Ethernet translate([wall + clear + 5, -1, floor_t + 3]) cube([55, wall + 2, wall_h]); // USB-C, HDMI, audio } } module standoffs() { color("#c9a227") for (p = holes) translate([wall + clear + p[0], wall + clear + p[1], floor_t]) difference() { cylinder(d = 6, h = 3, $fn = 32); translate([0, 0, -1]) cylinder(d = 2.5, h = 5, $fn = 24); } }
with parts: { tray: 'tray();', standoffs: 'standoffs();' }. OpenSCAD itself meshes it on every read, in-process as WebAssembly with the Manifold backend, so booleans are exact and every edge is sharp. color() is the tint; each named part is a render group a hinge in movers can swing. The same ref serves the orbit view, the .glb, the engine packs and model.stl at 91 × 62 × 14 mm as written; model.scad hands the source back unchanged. Change wall_h with update_sketch and the readout names the one part that moved. For what OpenSCAD cannot say, a blended join, a stroked dent, seeded noise, the source calls mojulo_field(" ") and a field solid from the same recipe is baked in at that spot.
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