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)
MCP server for the FFmpeg Micro video transcoding API — create, monitor, download transcodes.
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.
FFMPEG_MICRO_API_KEY비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
웹, 데스크톱, 모바일의 Claude에서 쓸 수 있어요:
https://mcp.ffmpeg-micro.comClaude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"ffmpeg-micro": {
"command": "npx",
"args": [
"-y",
"@ffmpeg-micro/mcp-server"
],
"env": {
"FFMPEG_MICRO_API_KEY": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add --transport http ffmpeg-micro https://mcp.ffmpeg-micro.comclaude mcp add ffmpeg-micro --env FFMPEG_MICRO_API_KEY=your-value -- npx -y @ffmpeg-micro/mcp-server실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"ffmpeg-micro": {
"url": "https://mcp.ffmpeg-micro.com"
}
}
}프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"ffmpeg-micro": {
"command": "npx",
"args": [
"-y",
"@ffmpeg-micro/mcp-server"
],
"env": {
"FFMPEG_MICRO_API_KEY": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"ffmpeg-micro": {
"type": "http",
"url": "https://mcp.ffmpeg-micro.com"
}
}
}프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"ffmpeg-micro": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@ffmpeg-micro/mcp-server"
],
"env": {
"FFMPEG_MICRO_API_KEY": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요:
https://mcp.ffmpeg-micro.com메뉴 이름은 요금제나 시기에 따라 조금 다를 수 있어요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
아직 이 서버의 도구 목록을 읽지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 도메인 ffmpeg-micro.com 기준으로 확인되었어요(레지스트리가 도메인 소유를 확인해요)
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 점검하지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요
아직 점검하지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요
아직 점검하지 않았어요. 엔드포인트를 점검할 때 도구 목록을 읽어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
입력해야 할 값: FFMPEG_MICRO_API_KEY
둘 다 가능해요. 내 컴퓨터에 설치해도 되고, 클라우드 버전에 연결해도 돼요.
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.4.3)은 20일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. URL로 연결되기 때문에, 원격 MCP 서버를 지원하는 앱(예: 개발자 모드의 ChatGPT)에서도 쓸 수 있어요.
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.
A Model Context Protocol server that lets AI agents — Claude Code, Claude Desktop, Cursor, Windsurf, VS Code, and any other MCP-compatible client — create, monitor, and download video transcodes through the FFmpeg Micro REST API.
Exposes tools that map onto FFmpeg Micro's public API:
| Tool | What it does |
|---|---|
transcode_video | Create a transcode job from one or more input videos (gs:// or https://). Supports quality/resolution presets and raw FFmpeg options. |
get_transcode | Fetch the current state of a single job. |
list_transcodes | List jobs with optional status, page, limit, since, until filters. |
cancel_transcode | Cancel a pending or processing job. |
get_download_url | Generate a 10-minute signed HTTPS URL for a completed job's output file. |
transcode_and_wait | Convenience: create a job, poll until it finishes, return the signed download URL in one call. |
request_upload_url | Step 1 of the direct-upload flow. Returns a presigned HTTPS URL that the host PUTs the file bytes to. |
confirm_upload | Step 2 of the direct-upload flow. Returns the final gs:// URL plus probe metadata, ready to use as a transcode/transcribe input. |
run_blueprint | Start a blueprint run — a pre-built video workflow (captioning, resizing, watermarking, ads, and more). |
get_blueprint_run | Fetch a blueprint run's status, step, and output URLs (multi-output blueprints return labeled outputs). |
run_blueprint_and_wait | Convenience: start a blueprint run and poll until it completes, fails, or pauses for transcript review. |
continue_blueprint_run | Resume a caption-video run paused in awaiting_review by submitting the approved SRT transcript. |
Blueprints are pre-built workflows behind POST /v1/blueprints/{slug}/runs. The tool descriptions document each blueprint's input fields. Notes:
product-ad) charge tokens; a 402 insufficient_tokens response means the account needs a token pack (dashboard).caption-video pauses in awaiting_review with the transcript (srt_text) so the agent can review/edit before rendering; resume with continue_blueprint_run.listing-kit, hook-variants) return an outputs array of {label, url} — prefer it over output_url when present.The request_upload_url + confirm_upload pair lets an MCP host upload a local file to the FFmpeg Micro storage bucket without dealing with raw API keys or gs:// URLs:
request_upload_url with {filename, contentType, fileSize} → receives a short-lived presigned HTTPS URL.Content-Type.confirm_upload with {filename: , fileSize} → receives the final gs://... fileUrl.fileUrl to transcribe_audio / transcode_video / transcode_and_wait.Add this to your project's .mcp.json (or your MCP client's config):
{
"mcpServers": { "ffmpeg-micro": { "type": "http", "url": "https://mcp.ffmpeg-micro.com" } } }
That's it. The first time your AI tool connects, it will open a browser window for you to sign in with your FFmpeg Micro account via OAuth. After you approve, the token is cached and you won't be asked again.
No API keys to copy, no environment variables to set.
The MCP server supports OAuth 2.1 with PKCE and dynamic client registration. Your MCP client handles the entire flow automatically:
/.well-known/oauth-authorization-serverThis is the default when you use the config above with no headers or env block.
If you prefer to use an API key directly (e.g., for automation or CI), you can pass it as a Bearer token:
{
"mcpServers": { "ffmpeg-micro": { "type": "http", "url": "https://mcp.ffmpeg-micro.com", "headers": { "Authorization": "Bearer your_api_key_here" } } } }
Get your API key from the dashboard.
Runs the server as a local process using npx. Requires Node.js 22.14 or later.
{
"mcpServers": { "ffmpeg-micro": { "command": "npx", "args": ["-y", "@ffmpeg-micro/mcp-server"], "env": { "FFMPEG_MICRO_API_KEY": "your_api_key_here" } } } }
npx -y fetches the latest version each time. Any MCP client that supports stdio servers works with this config.
The HTTP config (OAuth) works with any MCP client that supports streamable HTTP transport:
The stdio config works with any MCP client that supports stdio transport.
Once connected, you can ask things like:
b5f5a9c0-9e33-4e77-8a5b-6a0c2cd9c0b3."git clone https://github.com/ffmpeg-micro/ffmpeg-micro-mcp.git
cd ffmpeg-micro-mcp ./scripts/setup.sh
setup.sh installs dependencies, builds, and wires up the git hooks.
Point your MCP client at the local build to iterate:
{
"mcpServers": { "ffmpeg-micro-dev": { "command": "node", "args": ["/absolute/path/to/ffmpeg-micro-mcp/dist/index.js"], "env": { "FFMPEG_MICRO_API_KEY": "…" } } } }
The MCP Inspector is the fastest way to iterate on tool schemas and responses:
npx @modelcontextprotocol/inspector node dist/index.js
To run the HTTP server locally against a local API gateway:
FFMPEG_MICRO_API_URL=http://localhost:8081 npm run serve
FFMPEG_MICRO_API_KEY=your_key npm run test:integration
Integration tests hit the real FFmpeg Micro production API. They are read-only (no jobs are created).
Unit tests use a mocked fetch, so they prove tool registration + Zod schemas + URL paths but not that the wire shapes match what the gateway actually returns. Two smoke scripts exercise the full request_upload_url → PUT → confirm_upload flow against a real MCP server using a real API key. Run them in order — stdio first (fastest signal), then a deployed HTTP server before/after merge:
# 1. stdio (local dist build) — spawns dist/index.js as a subprocess
npm run build FFMPEG_MICRO_API_KEY=your_key node scripts/smoke-upload-stdio.mjs
npm run serve, Vercel preview, or prod)FFMPEG_MICRO_API_KEY=your_key MCP_URL=https://mcp.ffmpeg-micro.com/ \ node scripts/smoke-upload-http.mjs
Both scripts hit the production API by default and consume billable minutes (the stdio script chains into transcribe_audio for an end-to-end check). Pass a small file like 15-second.mp3 to keep the cost negligible.
A third script smoke-tests the blueprint tools (run_blueprint + get_blueprint_run polled to completion on resize-format, then run_blueprint_and_wait on hook-variants to exercise multi-output). It uses FFmpeg-lane blueprints only, so it consumes plan compute minutes but no tokens:
npm run build
FFMPEG_MICRO_API_KEY=your_key node scripts/smoke-blueprints-stdio.mjs
Vercel preview deployments are gated by Deployment Protection by default. To exercise the HTTP smoke script against a preview URL, generate a Protection-Bypass-for-Automation token in the project's Vercel settings and pass it via VERCEL_BYPASS:
FFMPEG_MICRO_API_KEY=your_key \
MCP_URL=https://your-preview.vercel.app/ \ VERCEL_BYPASS=your_bypass_token \ node scripts/smoke-upload-http.mjs
The script sends the token as the x-vercel-protection-bypass header on every request. It does not send x-vercel-set-bypass-cookie: true — that variant triggers a 307 cookie-setting redirect on POST that the MCP SDK's StreamableHTTPClientTransport does not follow, so the request fails. The header alone returns 200 directly without the redirect dance.
Releases are published to npm via trusted publishing and to the MCP Registry as com.ffmpeg-micro/mcp-server, authenticated via an Ed25519 DNS TXT record on ffmpeg-micro.com. The corresponding private key lives in the MCP_PRIVATE_KEY GitHub Actions secret. The npm side uses OIDC trusted publishing, so no npm token is stored.
Releases are automated via Changesets. Contributors don't manually bump versions, tag commits, or run publish commands — they attach a changeset to their PR and the release pipeline handles the rest.
Every PR that changes shipped code must include a changeset. A CI check enforces this.
# While working on your PR:
npx changeset
The CLI prompts for bump type (major/minor/patch) and a short summary. It writes a markdown file under .changeset/ — commit that file with your PR.
Escape hatches for non-release PRs (docs, CI, internal refactor, test changes with no behavioral impact):
no-changeset label to the PR, ornpx changeset --empty to explicitly declare "no release needed."You don't manually cut releases. The pipeline does it:
main. When pending changesets exist, it opens (or updates) a chore(release): version packages PR authored by the action. That PR:changeset version to consume the pending changesetspackage.jsonserver.json and src/version.ts via scripts/sync-server-version.mjsCHANGELOG.mdmain.changesets/action detects the version bump and:changeset publish, which runs npm publish (OIDC trusted publishing, with provenance attestation) only if that version isn't on npm yet, so every other push to main passes this step앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요