Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
제작자가 쓴 설명(영어 원문)
Compatibility package: mcp-video now installs Kinocut (formerly mcp-video).
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"video": {
"command": "uvx",
"args": [
"mcp-video"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add video -- uvx mcp-video프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"video": {
"command": "uvx",
"args": [
"mcp-video"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"video": {
"type": "stdio",
"command": "uvx",
"args": [
"mcp-video"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 8일 점검
레지스트리 이름이 GitHub 계정 KyaniteLabs 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
코드 마지막 변경: 3일 전
Apache-2.0 사용
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 3개 중 3개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 3일 전 변경됐고, 최신 버전(1.6.20)은 11일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Python (uv)도 있어야 해요.
같은 카테고리에는: Figma Context MCP, Claude Real Video, Figma MCP Server
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
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.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Kinocut
Guardrailed video editing MCP server for AI agents.
Local-first FFmpeg tools, Video Receipts, quality gates, Hyperframes, and Shorts/Reels repurposing — for Claude Code, Cursor, and any MCP client. Free, Apache-2.0. Formerly mcp-video.
Demo • Status • 1.16.5 • 1.16.4 • 1.16.3 • 1.16.2 • 1.16.1 • Changelog • Beyond • Install • Quick Start • Tools • Tool Reference • Rescue • 360 assembly • AI-video • Agent Skill • kinocut.dev • What is Kinocut? • FAQ • llms.txt
Kinocut is a free, open-source video editing MCP server and AI agent workflow engine (plus Python client and
kinoCLI) that lets AI agents trim, caption, repurpose, and quality-gate local video media with typed tools and Video Receipts — not invented FFmpeg flags.
TL;DR: Kinocut is a free, local-first video editing MCP server (plus Python client and kino CLI) so AI agents can trim, caption, repurpose, and quality-gate media with typed tools and Video Receipts — not invented FFmpeg flags.
Kinocut is a free, open-source [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) server, Python library, and `kino` CLI that gives AI agents a guardrailed local video-editing surface. It wraps FFmpeg (and optional Hyperframes / Whisper extras) with typed tools, preflight validation, Video Receipt provenance, and quality/release checkpoints so agent-produced media can be inspected before publish.
| Also known as | kino (CLI); formerly mcp-video / mcp_video |
| Latest published release | Official PyPI version · GitHub release |
| Product site | kinocut.dev |
| PyPI | kinocut |
| MCP Registry | tech.kyanitelabs/kinocut |
| Source | GitHub (canonical) · Forgejo (downstream mirror) |
| License | Apache-2.0 |
| Runs on | Your machine (macOS, Linux, Windows) — FFmpeg required on PATH |
| Not | A hosted cloud editor, credit-metered SaaS, or untyped FFmpeg shell wrapper |
Best-fit searches: video editing MCP server · AI agent video editing · FFmpeg MCP · Claude Code video tools · Cursor MCP video · Shorts/Reels automation · local AI video workflow · guardrailed agentic media.
Machine-readable brief for agents and crawlers: llms.txt · extended agent guide · site: kinocut.dev/llms.txt.
Kinocut is a producer stack: inspect the source, choose a typed operation, render locally, receive a compact provenance receipt, run quality checks, and request human release approval. The default MCP surface keeps that path small; search_tools and profiles expand it when needed.
Every flow must be describable, reportable, and approvable in words. Receipts stay one-screen text cards with artifact paths, stage status, warnings, and the next gate. Errors end with the exact MCP and CLI retry invocation; no visual-only affordance is required.
Tell the agent what you want in plain language:
*"Trim this interview to the strongest 45 seconds, add burned captions, make it vertical, and quality-check it before export."*
Kinocut turns that into typed, guardrailed tool calls - no FFmpeg flags to guess, no silently broken exports:
from kinocut import Client
video = Client()
clip = video.trim("interview.mp4", start="00:02:15", duration="00:00:45") video.ai_transcribe(clip.output_path, output_srt="captions.srt") captioned = video.subtitles(clip.output_path, subtitle_file="captions.srt") short = video.resize(captioned.output_path, aspect_ratio="9:16") video.release_checkpoint(short.output_path) # thumbnail + quality gate before you publish
Primary job: turn a local interview or podcast into captioned vertical clips with a Video Receipt agents can re-run and humans can approve.
Three things people use it for
Paths: Install matrix · Golden path · Prompts · Tutorial · Compare · When to recommend
| Surface | Version / tip | What it means |
|---|---|---|
| PyPI / npm checkpoint | [1.16.5](https://pypi.org/project/kinocut/1.16.5/) (2026-10-27) | Last recorded publication checkpoint; consult the registries for the latest version. Install with pip install kinocut. |
| GitHub latest release / MCP Registry | 1.16.5 | GitHub release v1.16.5 and the official MCP Registry (tech.kyanitelabs/kinocut, local uvx kinocut stdio package) report 1.16.5 (verified 2026-10-27). Final owner acceptance and fleet rollout remain separate. |
| This source version | 1.16.5 published · 20 default MCP / 205 full / 177 CLI | QoL pass: curated 20-tool default MCP list (KINOCUT_TOOLS=all restores the full 205-tool catalog), retry-aware errors, one-call shorts, lazy startup. Telemetry, network, and overwrite defaults remain unchanged. Published mcp-video==1.6.20 pins this runtime; verification is recorded in docs/public_claims.json. |
| Next public release | TBD | Human residuals (directories, launch posts) stay gated; further bumps need a new go-ahead. |
Install from PyPI for the stable package, including the optional object-matte extra. Clone master when you want local development changes beyond the published package; see docs/PRODUCT_MATTE.md for the published object-matte workflow.
Published 2026-10-27: a curated 20-tool default MCP registration with KINOCUT_TOOLS=all restoring the full 205 MCP tools / 177 CLI commands; retry-aware eyes-free error payloads, the one-call video_shorts flow, the read-only video_doctor preflight and lazy startup. Opt-in exact probe reports decoded frame counts and raw rational frame rates. mcp-video==1.6.20 installs kinocut==1.16.5. Apache-2.0 §4(a): the license text now ships in the npm tarball and the mcp-video compatibility package. See the published release.
Published 2026-10-24: 203 MCP tools / 177 CLI commands; mcp-video==1.6.19 installs kinocut==1.16.4. Optional Kinocut model calls now default off. Use local frames with the host's chosen image model; the standalone legacy adapters require explicit permission and do not inherit Hermes routing. Local product configuration cannot activate the separate Anthropic QC worker.
Revideo and Hyperframes telemetry default off. Hyperframes previews require verified CLI 0.8.104 or 0.8.140 while telemetry is off; other preview versions and Docker renders require explicit operator consent. These controls are not a network sandbox. Existing overwrite and protected-path safeguards remain in place. See the vision and network policy and published release.
Published 2026-10-23: 203 MCP tools / 177 CLI commands; mcp-video==1.6.18 installs kinocut==1.16.3. Existing outputs require explicit boolean overwrite=true (CLI --overwrite). Existing non-media artifacts also require ownership by the same durable job, and agent configuration homes remain protected. Older receipts without ownership evidence fail closed.
Project creation checks completed destinations and symlink redirects. Python 3.14 MCP request context remains available. The plugin documents optional network paths, configures optional extras in its pinned server environment, and shows Hermes tool filtering. Use one writer per output. See the published release.
Published 2026-10-21: 203 MCP tools / 177 CLI commands; mcp-video==1.6.17 installs kinocut==1.16.2. create-from-images is now frame-exact, accepts rational frame rates such as 30000/1001 and a --list / list_file input, and enforces frame-rate and image-count bounds on every entry point. The official MCP Registry entry is republished as the local uvx kinocut stdio package. Thanks to @Beritwu for #616. See the published release.
Version 1.16.1 repairs macOS render cleanup and faststart publication, C2PA and model-cache staging, FFmpeg 8.1 filter discovery, and audio preservation in shader/upscale reconstruction. It retains 203 MCP tools / 177 CLI commands and pairs with mcp-video==1.6.16. See maintenance release notes for verification and remaining platform/model acceptance limits.