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)
Compose and render video from one JSON template. Deterministic, runs locally, no generative model.
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.
LECLAP_MCP_OUTPUT_DIR, LECLAP_MCP_MEDIA_DIR, LECLAP_MCP_RENDER_TIMEOUT_MS, LECLAP_MCP_ALLOW_REMOTION, LECLAP_MCP_REMOTION_ENTRYDu 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": {
"video": {
"command": "npx",
"args": [
"-y",
"@leclap/mcp"
],
"env": {
"LECLAP_MCP_OUTPUT_DIR": "your-value",
"LECLAP_MCP_MEDIA_DIR": "your-value",
"LECLAP_MCP_RENDER_TIMEOUT_MS": "your-value",
"LECLAP_MCP_ALLOW_REMOTION": "your-value",
"LECLAP_MCP_REMOTION_ENTRY": "your-value"
}
}
}
}Führe das in deinem Terminal aus
claude mcp add video --env LECLAP_MCP_OUTPUT_DIR=your-value --env LECLAP_MCP_MEDIA_DIR=your-value --env LECLAP_MCP_RENDER_TIMEOUT_MS=your-value --env LECLAP_MCP_ALLOW_REMOTION=your-value --env LECLAP_MCP_REMOTION_ENTRY=your-value -- npx -y @leclap/mcpTrag die leeren Werte ein, bevor du ihn startest.
In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"video": {
"command": "npx",
"args": [
"-y",
"@leclap/mcp"
],
"env": {
"LECLAP_MCP_OUTPUT_DIR": "your-value",
"LECLAP_MCP_MEDIA_DIR": "your-value",
"LECLAP_MCP_RENDER_TIMEOUT_MS": "your-value",
"LECLAP_MCP_ALLOW_REMOTION": "your-value",
"LECLAP_MCP_REMOTION_ENTRY": "your-value"
}
}
}
}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": {
"video": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@leclap/mcp"
],
"env": {
"LECLAP_MCP_OUTPUT_DIR": "your-value",
"LECLAP_MCP_MEDIA_DIR": "your-value",
"LECLAP_MCP_RENDER_TIMEOUT_MS": "your-value",
"LECLAP_MCP_ALLOW_REMOTION": "your-value",
"LECLAP_MCP_REMOTION_ENTRY": "your-value"
}
}
}
}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.
LECLAP_MCP_OUTPUT_DIR envLECLAP_MCP_MEDIA_DIR envLECLAP_MCP_RENDER_TIMEOUT_MS envLECLAP_MCP_ALLOW_REMOTION envLECLAP_MCP_REMOTION_ENTRY envDiese 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 über die Domain leclap.dev bestätigt (die Registry prüft, wem die Domain 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 (0.3.4) kam vor 2 Monaten 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.
Connect your AI agent to LeClap’s video engine. Discover samples, author a template, validate it, inspect real frames, revise and render. Start with the agent guide.
An MCP server that exposes the ffmpeg-video-composer engine as agent-callable video tools.
An AI agent (Claude Desktop, Cursor, …) is the LLM; this server helps it author a customized template with nice effects from the schema, then validates and renders it deterministically to an mp4. The server includes the 50 showcase samples through the shared packaged catalog, with creative direction, descriptors and required inputs. It works without the app or private creative-kit at runtime. Remotion-assisted authoring is an optional path. The result is _agent-composable, deterministic, reproducible_ video — the opposite of generative video models, which sample rather than render.
| Tool | Description |
|---|---|
get_samples | Without id: sample metadata and required inputs, filtered by category/backend/query → { samples }; with id: that sample with its self-contained template JSON |
get_template_schema | The JSON Schema for a template descriptor + a short authoring guide |
get_motion_catalog | Motion presets, camera, graphics, transitions, easing/time grammar, themes, platforms, genre doctrine and scene blueprints; { query, kind? } → ranked matches (a query matching nothing is logged to the catalog gap log) |
validate_template | Dry-run an inline descriptor (no render) → { valid, sectionCount, orientation, requiredClips, formFields, fields?, geometry?, featureWarnings? }; include: ["resolved"] adds resolved (the descriptor for fields/format), include: ["timeline"] adds timeline (sections, motion events, beats, cues) |
compose_video | Validate an inline descriptor and render (one format of it, optionally) → { outputPath, durationSeconds, sizeBytes, videoCodec, audioCodec, renderId }, plus a resource_link to the mp4 |
render_frames | Render a native template and return still frames as PNG images + paths: at, atTransitions, perSection, sheet, safe, zoom, variants, looks |
edit_template | JSON Patch operations and/or registered effectProps by section name over inline JSON, under expectedRevision, all-or-nothing → { template, revision, changedPaths, changedSections? } |
probe_media | Inspect a local media file → codecs, duration, sample rate, size, and HDR / colour / bit depth / VFR / rotation traits |
extract_style | Reference image/clip under the media dir → { theme, styleGuide, confidence }: palette roles + WCAG contrast, grain, pacing (palette and pacing only) |
extract_structure | A reference clip's format (shots, rhythm, transitions, grounds, layout) as binding rules for a template. Format only; samples every 0.25 s for up to 2 minutes. |
extract_brand | A website's brand kit (name, colours, fonts, logo) as a theme and binding rules, saved under /brand-kits/ /. Needs --allow-network. |
analyze_music | Measure a local music file → { bpm, offset, beatsPerBar, confidence, usable, cues, globalBeats } for global.beats and cue:drop |
analyze_sound | Render an sfx[].sound (composed, or a preset with variations) → length, peak/RMS dBFS, centroid, high/low energy shares, attack, advisories + spectrogram and waveform PNGs |
transcribe_media | Transcribe a local audio/video file's speech with whisper.cpp, locally → { words, srt, language, confidence, advice } to pin into subtitles.words |
get_capabilities | Local FFmpeg capability report (listings + one-frame probes) → each feature yes/no/unknown with a fix |
open_in_builder | Template → a leclap.dev/studio/builder#t=… link for a person to edit it; the template rides in the URL fragment (never sent to a server) → { url, length, mediaToRebind, warnings } |
render_remotion_clip | _(bonus, opt-in)_ Render a composition from your own Remotion project → an mp4 clip for a project_video section |
analyze_sound seeds a sound's noise and jitter with seed (default 0). To hear the exact render a template will play, pass the template's global.seed as seed and the cue's path as cue: "sections.intro.sfx[0]" for the first cue of the section named intro, "global.sfx[2]" for the third global cue. The tool then seeds the sound as the mix seeds that cue and returns the seed it used. Levels are floored at -120 dBFS, so a silent sound reads -120 and raises sound_silent.
Typical agent flow: get_samples → get_samples { id } → inspect requirements and customize media/copy → get_template_schema → validate_template (iterate until valid) → render_frames (look at the result; check safe zones with safe) → compose_video → read the returned outputPath. Author a fresh descriptor from the schema when no sample fits.
render_frames renders through a per-section cache under /.section-cache, so a second look after a small edit re-encodes only the sections that changed. It runs in the render worker under the same media-dir checks, slot cap, timeout and cancellation as compose_video, writes PNGs to /frames- /, and inlines at most eight images (the sheets when sheet is set). Moments are seconds or time references on the whole video: "intro.end" (a section or an element id), "50%", "beat:8", "cue:drop + 0.1". validate_template with include: ["timeline"] lists the moments worth picking.
validate_template also reports, render-free, text that would run off the frame or out of title-safe, collide with other text, sit under a band, be too small, lack contrast, or sit over footage with no box, outline or shadow. Those findings arrive on the optional geometry field — one line each, saying what to change, and the field is absent entirely when there is nothing to fix. They are advisory: valid stays true. A line ending (approx: …) says why it is an estimate: the font could not be read, the text carries a {{ variable }} that only resolves at render time, or a section declares no duration. When a font is not bundled, the server fetches it from the LeClap asset catalog (5s timeout); offline, it estimates.
Pass render: true to validate_template to also render the sections that hold text and measure their contrast from real pixels — it settles text over images, grades and looks that the render-free check can only call unknown. It costs seconds (two renders of those sections), goes through the same media-dir sandbox as compose_video, reads assets from the media dir, and runs in the same forked render worker under the same timeout. It adds a render field (measured, seconds, and unavailable when it could not render — no FFmpeg with drawtext, a failed render, or a timeout); the render-free findings come back either way.
get_samples is always available, even with Remotion disabled. It reads packaged data only: no media downloads, effect execution or repository access. Without id it lists, filtered by optional category (templates, typography, effects, app-demos, overlays, evidence), backend (native, remotion) and case-insensitive query (up to 4000 characters). With id (1–200 characters) it returns that sample.
{ "name": "get_samples", "arguments": { "category": "app-demos", "backend": "native" } }
{ "name": "get_samples", "arguments": { "id": "web-app-promo" } }
Results include identical JSON in text and structuredContent; with id, the descriptor sits at template alongside its metadata. Unknown IDs return isError with a discovery hint. Required inputs include named clips and durations/capture hints, form fields and limits, variables/defaults/placeholders, assets, effective preset font files (source: "preset") and versioned effects. Replace the sample copy, supply clips by userVideoPaths and form values by fields, customize global.variables, then pass template to validate_template and compose_video. Referenced partials are embedded in the descriptor.
The 25 native samples use FFmpeg; the 10 registered effect samples require --allow-remotion, Remotion peers and a trusted --remotion-entry. Effects with customCatalog: true also require the operator's --effect-catalog and the matching composition in that entry. Sample discovery does not enable execution, install a catalog or supply React source. Inspect requirements.setup and, after operator setup, get_effect_schema before editing effect props. See operator catalogs.
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