Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Description from the maker (original English)
MCP server for the FFmpeg Micro video transcoding API — create, monitor, download transcodes.
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We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
FFMPEG_MICRO_API_KEYValues marked secret are keys to your account. Never share them or paste them into a chat.
You need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Works in Claude on the web, desktop and mobile:
https://mcp.ffmpeg-micro.comOpen Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"ffmpeg-micro": {
"command": "npx",
"args": [
"-y",
"@ffmpeg-micro/mcp-server"
],
"env": {
"FFMPEG_MICRO_API_KEY": "your-value"
}
}
}
}Run this in your terminal
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-serverFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"ffmpeg-micro": {
"url": "https://mcp.ffmpeg-micro.com"
}
}
}Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"ffmpeg-micro": {
"command": "npx",
"args": [
"-y",
"@ffmpeg-micro/mcp-server"
],
"env": {
"FFMPEG_MICRO_API_KEY": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"ffmpeg-micro": {
"type": "http",
"url": "https://mcp.ffmpeg-micro.com"
}
}
}Paste into .vscode/mcp.json in your project
{
"servers": {
"ffmpeg-micro": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@ffmpeg-micro/mcp-server"
],
"env": {
"FFMPEG_MICRO_API_KEY": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
ChatGPT connects only to servers on the internet:
https://mcp.ffmpeg-micro.comMenu names can differ slightly by plan and over time.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
We have not read this server’s tool list yet; we check endpoints in rotation.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026
The registry name was verified against the domain ffmpeg-micro.com (the registry checks domain ownership)
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
Not checked yet; we check endpoints in rotation
Not checked yet; we check endpoints in rotation
Not checked yet; we read the tool list when we check the endpoint
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
You need to fill in: FFMPEG_MICRO_API_KEY
Either way works: install it on your computer or connect to the cloud version.
The GitHub repo last changed –, and the latest version (0.4.3) reached the registry 20 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.
In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
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.
Figma’s official server gives AI your design data so it can turn designs into code or describe them accurately. Connects to Figma’s cloud.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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 stepOnly the beginning is shown; read the rest in the repo