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)
Local video editing for AI agents: cut, caption, analyse, reuse viral formats. FFmpeg, no cloud.
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": {
"wecut": {
"command": "npx",
"args": [
"-y",
"wecut",
"mcp"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add wecut -- npx -y wecut mcpIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"wecut": {
"command": "npx",
"args": [
"-y",
"wecut",
"mcp"
]
}
}
}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": {
"wecut": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"wecut",
"mcp"
]
}
}
}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 13kailouis-stack 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 (0.1.0) kam vor 14 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.
WeCut is a free, open-source video editing engine for AI agents. The agent writes a small JSON project; wecut renders it with the FFmpeg already on the machine. It cuts, captions, reframes and mixes short-form video, and it can analyse a video that went viral and reuse its format as a template on new footage. CLI and MCP server. No cloud, no API key, no GPU, no telemetry.
Things you can ask an agent that has WeCut:
talk.mp4 and put the music under it, quieter when he speaks."npx -y wecut doctor # is FFmpeg ready?
npx -y wecut guide # the full guide for agents npx -y wecut probe media/talk.mp4 # duration, size, fps, audio npx -y wecut validate project.json # errors with JSON pointers and fix hints npx -y wecut preview project.json --at 1,5,9 # small JPG frames to look at npx -y wecut render project.json # out/clip.mp4 npx -y wecut mcp # the same, as an MCP server on stdio
Every command prints one JSON object on stdout. Exit codes: 0 ok, 2 invalid input, 3 missing dependency, 4 render failed, 5 over a limit.
WeCut Studio is an independent static web editor. Choose local clips, trim and reorder them, add text and music, then download a portable template for an agent on another device. Editing runs in the browser; rendering runs on the agent's computer. A public editor needs only static file hosting, not a rendering server. A localhost URL itself is not public.
Run python -m http.server 8770 --bind 127.0.0.1 --directory studio from this checkout and open http://127.0.0.1:8770. No build or backend is needed. The template file is shared through your agent chat; public template storage and project autosave are not included. Bundle import requires the 0.2.0 source/build; published npm 0.1.0 does not include it yet.
The quick way, for Claude Code, Claude Desktop, Codex, Cursor, Gemini CLI, Windsurf and Grok Build:
npx -y wecut setup # shows what it would change
npx -y wecut setup --apply # registers the MCP server and the skill, backs up each file it edits npx -y wecut setup --remove --apply
It only touches the clients it finds (or --client codex,cursor), keeps every other setting as it was, and reads each file back. Quit Claude Desktop before you run it: while it runs it can write its settings back and drop the entry. Run it again after updating WeCut, so the installed skill stays current. Or do it by hand:
WeCut needs FFmpeg 6 or newer with libass (npx -y wecut doctor checks it and says what to install):
sudo apt-get install -y ffmpegbrew install ffmpeg-full (the slim ffmpeg formula has no libass; WeCut finds ffmpeg-full on its own)winget install Gyan.FFmpegThen connect it to the agent you use. The MCP server works in a fixed workspace folder (--root, default: the folder it was started in); every media path is relative to it.
claude mcp add wecut -- npx -y wecut mcp
Or as a plugin, which also installs the WeCut skill:
/plugin marketplace add 13kailouis-stack/wecut
/plugin install wecut@wecut
Claude Desktop, Cursor, Windsurf, Cline, Roo Code, LM Studio and most other clients take this block in their MCP settings file. Give --root explicitly, because desktop apps start servers from an unpredictable folder.
{
"mcpServers": { "wecut": { "command": "npx", "args": ["-y", "wecut", "mcp", "--root", "/path/to/workspace"] } } }
code --add-mcp '{"name":"wecut","command":"npx","args":["-y","wecut","mcp"]}'
or in .vscode/mcp.json:
{ "servers": { "wecut": { "type": "stdio", "command": "npx", "args": ["-y", "wecut", "mcp"] } } }
codex mcp add wecut -- npx -y wecut mcp
or in ~/.codex/config.toml:
[mcp_servers.wecut]
command = "npx" args = ["-y", "wecut", "mcp"]
gemini extensions install https://github.com/13kailouis-stack/wecut
WeCut targets any agent with permission to run local programs and access the user's media. Product names do not determine compatibility: a terminal agent can use the CLI without MCP.
Grok Bot, Manus, Muse, OpenBot, Dots and Claude Duet are examples of requested integration targets, not a list of tested integrations. For each product, verify that its execution environment can run the WeCut binary and FFmpeg. A chat interface alone does not provide that access.
npx -y wecut guide # read this first: workflow, project format, templates
npx -y wecut doctor
Then follow the guide: probe, write project.json, validate, preview, render. In a fresh Linux sandbox install FFmpeg first with sudo apt-get install -y ffmpeg.
Clients that can launch a local MCP process can use wecut mcp over stdio; the client-specific examples above are not an allowlist. An HTTP-only MCP client needs a separate bridge: WeCut does not currently serve remote HTTP MCP. Browser-only agents need an execution environment before they can render. Node is needed for the npm launcher; a standalone binary does not need Node.
The skill in skills/wecut/SKILL.md teaches the workflow to agents that load skills (Claude Code, Claude apps, Codex, Cursor and others):
npx skills add 13kailouis-stack/wecut
npx -y wecut , or npm i -g wecut. npm installs only the binary for your platform (@wefluence/wecut- - ); there are no install scripts.SHA256SUMS.go build -o wecut ./cmd/wecutPrebuilt for Linux, macOS and Windows on x64 and arm64. Set WECUT_BINARY to use your own build through the npm launcher.
xfade), clip fadesgradients), like the dark fade behind captionsinset), colour washes for a stretch of the video, captions that light up word by word (reveal) with key words set apart (accent)--no-loudnorm skips it)wecut clone media/viral.mp4 --name news-card # -> recipes/news-card.json + out/clone/news-card/compare.jpg
clone turns a finished video into a template the way an editor would have built it: every cut and transition (flash, fade, dissolve, zoom, blur) at its exact time, each shot's framing (full screen, or shown whole on black or blurred bars) and push-in, the text cards with their colours, box, size, line spacing, padding, timing and entrance (fade, pop, slide or type), the caption style and position, the logo, and a talk that already plays under the opening B-roll. A steady beat is stored as beat indexes; recipe apply snaps those cuts onto replacement music. Each shot is a media slot, like a CapCut template. WeCut holds no model and no API key: it returns a review with the images and evidence behind each finding, and the agent running it looks and answers, so words, fonts, what each shot is and the kind of each transition come from real eyes and OCR is only a fallback. The comparison sheet puts the original beside the template filled with numbered stand-ins, so a person or a model can check it at a glance. People fill the slots in WeCut Studio; agents with wecut recipe apply. Details and the format: docs/clone.md. Cloning needs the 0.3.0 source/build; the published npm 0.1.0 does not have it yet.
wecut analyze media/viral.mp4 # shots, pace, hook, flashes, tempo, pauses + a frame sheet
wecut recipe list # templates: built-in and the workspace's recipes/*.json wecut recipe apply --input request.json # recipe + new clips, words, music -> project wecut recipe from-project my-edit.json --write recipes/my-style.json # "save as template"
analyze measures what a model cannot see in a transcript: where the cuts are (a white flash or a strobe counts once), how long the opening shot holds, whether cuts land on the beat, and whether the file ends with a silent platform outro. Its frame sheet, one frame per shot with timestamps, is how the agent reads the headline card, text style and framing.
A recipe (wecut.recipe/1, schema in schema/wecut.recipe.schema.json) is an edit's shape without its content: sections fed by roles (a 5 s hook with a headline card, then the body), text slots filled at apply time (with emphasis for the words to colour), caption and music style, effects on cuts. A recipe can carry its own default track, like a CapCut template; the music passed at apply time replaces it and "none" drops it. The built-in recipes copy the structure of formats that are common on Indonesian short-form video today: news-style reposts with a headline card, quote hooks, phrase-captioned vlogs, punched-in podcasts, photo slideshows.
Copy the structure, not the content: other creators' footage, wording and music stay theirs.
| Tool | Does |
|---|---|
wecut_doctor | checks FFmpeg, encoders and filters |
wecut_probe | duration, size, fps, audio of media files |
wecut_validate | checks a project; errors carry a JSON pointer and a hint |
wecut_preview | renders frames and returns them as images, so the model can look |
| wecut_render | renders M
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