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)
Video pipeline for AI agents: analyze with AI, transcode to MP4/HLS/DASH, edit, protect and deliver.
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.
Funktioniert in Claude im Web, auf dem Desktop und auf dem Handy:
https://mcp.qencode.com/mcpFühre das in deinem Terminal aus
claude mcp add --transport http qencode https://mcp.qencode.com/mcpIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"qencode": {
"url": "https://mcp.qencode.com/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": {
"qencode": {
"type": "http",
"url": "https://mcp.qencode.com/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.
ChatGPT verbindet sich nur mit Servern im Internet:
https://mcp.qencode.com/mcpMenünamen können sich je nach Tarif und mit der Zeit leicht unterscheiden.
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.
Die Tool-Liste dieses Servers haben wir noch nicht gelesen; wir prüfen die Endpunkte reihum.
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 qencode.com 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
Noch nicht geprüft; wir prüfen die Endpunkte reihum
Noch nicht geprüft; wir prüfen die Endpunkte reihum
Noch nicht geprüft; wir lesen die Tool-Liste, wenn wir den Endpunkt prüfen
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 in der Cloud des Entwicklers. Du verbindest dich übers Internet, ohne etwas zu installieren, aber was du sendest, läuft über die Server des Entwicklers.
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (1.2.0) kam vor 16 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Er verbindet sich per URL, also funktionieren auch Apps, die Remote-MCP-Server akzeptieren, zum Beispiel ChatGPT im Entwicklermodus.
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.
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.mdNur der Anfang wird gezeigt; den Rest findest du im Repo