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)
MCP server for the FFmpeg Micro video transcoding API — create, monitor, download transcodes.
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.
FFMPEG_MICRO_API_KEYAls geheim markierte Werte sind Schlüssel zu deinem Konto. Gib sie nie weiter und füge sie nie in einen Chat ein.
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.ffmpeg-micro.comÖffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"ffmpeg-micro": {
"command": "npx",
"args": [
"-y",
"@ffmpeg-micro/mcp-server"
],
"env": {
"FFMPEG_MICRO_API_KEY": "your-value"
}
}
}
}Führe das in deinem Terminal aus
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-serverTrag die leeren Werte ein, bevor du ihn startest.
In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"ffmpeg-micro": {
"url": "https://mcp.ffmpeg-micro.com"
}
}
}In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"ffmpeg-micro": {
"command": "npx",
"args": [
"-y",
"@ffmpeg-micro/mcp-server"
],
"env": {
"FFMPEG_MICRO_API_KEY": "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": {
"ffmpeg-micro": {
"type": "http",
"url": "https://mcp.ffmpeg-micro.com"
}
}
}In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"ffmpeg-micro": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@ffmpeg-micro/mcp-server"
],
"env": {
"FFMPEG_MICRO_API_KEY": "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.
ChatGPT verbindet sich nur mit Servern im Internet:
https://mcp.ffmpeg-micro.comMenünamen 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 ffmpeg-micro.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.
Das musst du eintragen: FFMPEG_MICRO_API_KEY
Beides geht: Installiere ihn auf deinem Computer oder verbinde dich mit der Cloud-Version.
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.4.3) kam vor 20 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.
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 stepNur der Anfang wird gezeigt; den Rest findest du im Repo