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)
Video pipeline for AI agents: analyze with AI, transcode to MP4/HLS/DASH, edit, protect and deliver.
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.
웹, 데스크톱, 모바일의 Claude에서 쓸 수 있어요:
https://mcp.qencode.com/mcp터미널에서 이 명령어를 실행하세요
claude mcp add --transport http qencode https://mcp.qencode.com/mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"qencode": {
"url": "https://mcp.qencode.com/mcp"
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"qencode": {
"type": "http",
"url": "https://mcp.qencode.com/mcp"
}
}
}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.qencode.com/mcp메뉴 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 qencode.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.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
제작자의 클라우드에서 실행돼요. 설치 없이 인터넷으로 연결하지만, 보내는 내용은 제작자의 서버를 거쳐요.
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(1.2.0)은 16일 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.
Model Context Protocol (MCP) server for the Qencode Transcoding API.
Connect any MCP-compatible AI client — Claude, Cursor, ChatGPT, Grok, Gemini — to your Qencode account and let it submit, monitor, and reason about transcoding jobs on your behalf.
Once your client is connected (see Connect a client), ask your agent in plain English:
Transcode
https://example.com/input.mp4as an HLS ladder at 1080/720/540/360. Put it in my R2 bucketvideos/demo/.
The agent picks the hls_abr recipe, fills in the per-rendition encoding params, submits via start_encode2_raw, and polls until the job is done.
There are no API keys to copy into client config — authentication is browser-based OAuth.
The connector uses standard OAuth 2.1 — no API keys in client config. On first use, your client opens a browser, you sign in to your Qencode portal account, pick a project, and approve the requested scopes. The client stores the token; subsequent calls are silent until the token expires.
Scopes the client should request at authorize time (published via Protected Resource Metadata):
| Scope | Purpose |
|---|---|
openid | OIDC identity |
profile | Display name |
email | Account email |
offline_access | Refresh token |
transcoding:read | get_job_status, list_jobs, docs tools |
transcoding:write | transcode_video, start_encode2_raw |
The RS enforces transcoding:read and transcoding:write on access tokens at the transport layer.
Your Qencode API keys never leave the portal. The MCP server derives a short-lived session token per request via an internal portal endpoint.
The same connector is deployed in two environments. Each has its own domains, accounts, projects, and credentials — sign in to the portal that matches the endpoint you connect to.
| Role | Production | QA (testing) |
|---|---|---|
| MCP endpoint (connect here) | https://mcp.qencode.com/mcp | https://mcp-qa.qencode.com/mcp |
| Portal (sign in / projects) | https://portal.qencode.com | https://portal-qa.qencode.com |
| Authorization server | https://auth.qencode.com | https://auth-qa.qencode.com |
| Qencode API | https://api.qencode.com | https://api-qa.qencode.com |
The instructions below use the production endpoint. To test against QA, swap in the QA URL and sign in at the QA portal.
Endpoint: https://mcp.qencode.com/mcp — same for every client below. Sign in to your Qencode account when the browser opens and approve access.
QA (internal testing): use
https://mcp-qa.qencode.com/mcpand sign in at the QA portal instead.
| Client | Where to add it | MCP URL / config |
|---|---|---|
| Claude (chat) | Message box → + → Connectors → Add connector | https://mcp.qencode.com/mcp |
| Claude Code | Terminal | claude mcp add --transport http qencode https://mcp.qencode.com/mcp |
| ChatGPT | Apps → search Qencode → Connect; or Developer Mode → Build app | Connector URL: https://mcp.qencode.com/mcp |
| Gemini | ~/.gemini/settings.json → mcpServers | "httpUrl": "https://mcp.qencode.com/mcp" — then /mcp auth qencode in the CLI |
| Cursor | Settings → Tools & MCP → New MCP Server (or ~/.cursor/mcp.json) | "url": "https://mcp.qencode.com/mcp" — restart Cursor after saving |
Cursor (mcp.json):
{
"mcpServers": { "qencode": { "url": "https://mcp.qencode.com/mcp" } } }
Gemini (settings.json):
{
"mcpServers": { "qencode": { "httpUrl": "https://mcp.qencode.com/mcp", "timeout": 30000, "trust": false } } }
Tip: sign in to portal.qencode.com in your browser before connecting — OAuth goes smoother.
Transcoding & jobs
| Tool | Description |
|---|---|
transcode_video | Submit a job from a source URL to one or more outputs. Convenience wrapper — auto-injects encoder_version: 2 (or 1 for VMAF) when omitted. |
start_encode2_raw | Escape hatch — submit a job with the full query JSON exactly as the Qencode API expects. |
get_job_status | One-shot status snapshot by task_token. |
get_job_status_detailed | Full, authoritative job status, including per-rendition progress and output details. |
list_jobs | Inline jobs card for the task_tokens of the current conversation — status badges, filters, expandable rows, output URLs. The card refreshes itself while any job is still running. |
fetch_job_result | Read the contents of a result file produced by an analysis job (transcript, VMAF report, metadata, categorization). |
wait_for_job | Deprecated. Returns immediately and points at list_jobs; kept so older conversations do not hit "tool not found". |
search_qencode_docs | Search the built-in knowledge base of recipes and reference docs. |
fetch_qencode_doc | Fetch the full content of a knowledge-base resource by qencode:// URI (tool-based counterpart to resources/read). |
Playback
| Tool | Description |
|---|---|
open_player | Render a playable result inline — progressive MP4/WebM or an HLS/DASH manifest. The server applies the calling host's sandbox policy, so which source origins are allowed depends on the client. |
refresh_jobs | The jobs card's own silent poll. Not meant to be called directly by an agent; it backs the card's auto-refresh and Refresh button. |
Media Storage
Bucket management and ingest for Qencode Media Storage. These ride the same OAuth grant as the transcoding tools — no extra scope and no re-consent.
| Tool | Description |
|---|---|
list_buckets | List the Media Storage buckets available to the account. |
create_bucket | Create a new bucket. Called only on an explicit request — not to satisfy a missing destination. |
list_objects | Browse the contents of a bucket. |
get_download_url | Return a time-limited download URL for an existing object. |
download_url_to_bucket | Server-side copy of a public URL into a bucket (ingest, no transcoding). |
The server ships a knowledge base of recipes and reference docs, exposed as MCP resources so the agent can fetch only what it needs. Notable URIs:
qencode://docs/best-practices — composition defaults the agent applies automaticallyqencode://docs/storage — destination compatibility matrix (Qencode S3, R2, AWS S3, Azure, B2, FTP/SFTP)qencode://docs/error-codes — error code → cause → fixqencode://docs/gotchas — non-obvious API quirksqencode://schema/digest — full attribute reference for start_encode2qencode://recipe/ — one per feature flow: ai_detection, ai_upscaling, audio_outputs, callbacks, clip_trim, codec_av1, codec_lcevc, drm_aes128, drm_buydrm, drm_doverunner, drm_expressplay, drm_fairplay_ezdrm, drm_forensic_watermark, drm_playready_ezdrm, drm_widevine_ezdrm, hdr_to_sdr, hls_abr, incremental_abr, mp4_ladder, per_title_encoding, refresh_abr_playlist, reliability, repack, rotate_deinterlace, smart_crop, smart_thumbnail, speech_to_text, stitching, subtitles, thumbnails, video_intelligence, video_metadata, vmaf_quality, vr_360, vr_mode, watermark_logo, waveformUse search_qencode_docs to discover the right recipe URI for a goal.
In clients that surface MCP prompts, 40 one-shot templates are available. Each tells the agent to read the matching qencode://recipe/... resource and submit via start_encode2_raw.
ABR / packaging: encode_hls_abr, encode_dash_abr, encode_mp4_ladder, encode_incremental_rung, encode_refreshing_playlist, remux_repack
Codecs / quality: encode_av1, encode_lcevc, tune_per_title, check_vmaf, ai_upscale_video, convert_hdr_to_sdr
Audio / images / text: extract_audio, generate_thumbnails, generate_smart_thumbnails, generate_waveform, transcribe, add_subtitles
Editing / framing: trim_clip, rotate_video, deinterlace_video, enable_smart_crop, add_watermark, add_html_overlay
Immersive: enable_vr_mode, inject_360_metadata
Analysis: get_video_metadata, analyze_video, detect_ai_generated
Probe / stitch: stitch_videos
Production hooks: enable_callbacks, enable_reliability
DRM: encode_aes128_hls, encode_widevine_ezdrm, encode_playready_ezdrm, encode_fairplay_ezdrm, encode_drm_buydrm, encode_drm_expressplay, encode_drm_doverunner, encode_forensic_watermark
transcode_video and start_encode2_raw accept source values with schemes https://, http://, s3://, or tus:. FTP/SFTP and private/metadata URLs are rejected at the tool boundary (SSRF defence). See docs/security/THREAT_MODEL.md for limitations.
Authentication is OAuth 2.1 only — there is no static-API-key mode. Your Qencode API keys never leave the portal; the server derives a fresh, short-lived session token per request via an internal portal endpoint. Source URLs are validated at the tool boundary (SSRF defence — see Source URL rules).
Full threat model and adversarial test coverage: docs/security/THREAT_MODEL.md.
uv venv && uv pip install -e ".[dev]"
NO_NETWORK=1 pytest -q # offline L1 + L2 + L5 (~900 tests) pytest -m protocol # MCP wire conformance only pytest -m unit # per-tool logic (FakeQencode) pytest -m security # OWASP MCP Top 10 adversarial suite
Protocol tests run fully offline (mocked Authorization Server, portal, and Qencode API). CI is the Jenkins job mcp_automated_tests (Jenkinsfile.manual): manual checkboxes for any layer, nightly L3 cron, weekly L4 cron.
Clients negotiate a version at initialize. This server targets [MCP 2025-11-25](https://modelcontextprotocol.io/specification/2025-11-25) as the primary version. CI also runs conformance tests against 2025-06-18 because JSON-RPC batching behavior differs between earlier revisions. We do not claim support for 2025-03-26 or older wire semantics beyond what the underlying SDK negotiates.
| Version | Support | Notes |
|---|---|---|
| 2025-11-25 | Primary | Streamable HTTP, resumable SSE where used |
| 2025-06-18 | CI matrix | Regression guard for mid-2025 clients |
| 2025-03-26 | Not targeted | Batching semantics differ from 2025-06-18 |
The connector users install is the hosted endpoint above. For Glama Servers / awesome-mcp-servers scoring only, this repo also ships qencode-mcp-inspect (stdio, dummy env, tools/call refused). That is not a supported client transport. See docs/glama-release.md.
docs/local-development.mddocs/testing.md and tests/README.md앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요