Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
作者による説明(英語の原文)
Transform video, audio and images, and generate media from prompts. FFmpeg, captions, models.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
RENDOBAR_API_KEY「秘密」と表示された値は、あなたのアカウントの鍵です。絶対に人に教えたり、チャットに貼り付けたりしないでください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
ウェブ、デスクトップ、モバイルのClaudeで使えます:
https://api.rendobar.com/mcpClaude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"rendobar": {
"command": "npx",
"args": [
"-y",
"@rendobar/mcp"
],
"env": {
"RENDOBAR_API_KEY": "your-value"
}
}
}
}ターミナルでこれを実行します
claude mcp add --transport http rendobar https://api.rendobar.com/mcpclaude mcp add rendobar --env RENDOBAR_API_KEY=your-value -- npx -y @rendobar/mcp動かす前に、空欄の値を入力してください。
プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"rendobar": {
"url": "https://api.rendobar.com/mcp"
}
}
}プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"rendobar": {
"command": "npx",
"args": [
"-y",
"@rendobar/mcp"
],
"env": {
"RENDOBAR_API_KEY": "your-value"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"rendobar": {
"type": "http",
"url": "https://api.rendobar.com/mcp"
}
}
}プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"rendobar": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@rendobar/mcp"
],
"env": {
"RENDOBAR_API_KEY": "your-value"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
ChatGPTがつなげるのは、インターネット上のサーバーだけです:
https://api.rendobar.com/mcpメニューの名前は、プランや時期によって少し違うことがあります。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
このサーバーのツール一覧はまだ読み取っていません。エンドポイントは順番にチェックしています。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月6日にチェック
レジストリ名はドメイン rendobar.com で確認済みです(レジストリがドメインの所有を確認しています)
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
まだチェックしていません。エンドポイントは順番にチェックしています
まだチェックしていません。エンドポイントは順番にチェックしています
まだチェックしていません。エンドポイントをチェックするときにツール一覧を読み取ります
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
入力が必要なもの:RENDOBAR_API_KEY
どちらでも使えます。パソコンにインストールしても、クラウド版につないでもかまいません。
GitHubリポジトリの最終更新は–、最新バージョン(1.13.0)がレジストリに届いたのは27 日前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。URLでつながるので、リモートMCPサーバーに対応したアプリ(開発者モードのChatGPTなど)でも使えます。
同じカテゴリには:Figma Context MCP, Claude Real Video, Figma MCP Server
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
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.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
@rendobar/mcp
Serverless media processing for AI agents.
The official Model Context Protocol server for Rendobar.
Docs · npm · Glama · Discord
@rendobar/mcp is the official Model Context Protocol server for Rendobar, a serverless media processing API. The server runs locally over stdio and reads files straight from your disk, so an AI agent can take a file off your machine, process it on Rendobar's infrastructure, and hand back a hosted URL.
Rendobar covers both sides of media work.
Transform what you have. Run any FFmpeg command against video, audio or images the way you would write it locally. Inspect a file and get a normalized summary plus the full ffprobe report. Compose video from a declarative JSON timeline. Compress to a target size or quality, where the encoder searches candidate encodes and returns the smallest file that clears the bar. Burn in subtitles from SRT, VTT or ASS, or let it transcribe when none is given.
Generate what you do not. Create an image from a text prompt on hosted open-weight diffusion models. Edit up to four reference images from a written instruction, no masks and no coordinates. Upscale on a one-step diffusion restoration model that reconstructs detail rather than only sharpening. The same model-backed layer drives the transcription and keyword highlighting behind animated captions, so this is not an image-only capability.
The job list grows over time, so this README names families rather than types. list_job_types reads the current set live from the registry on every call.
Published to npm as @rendobar/mcp and to the official MCP Registry as com.rendobar/mcp.
You: Mute the first 3 seconds of
intro.mp4.
The agent tells you to install FFmpeg. Then you go looking for how to gate a filter on a timestamp, land on volume=enable='lt(t,3)', and lose another few minutes to quote escaping in your shell. Nobody remembers that syntax, which is the problem.
You: Mute the first 3 seconds of
intro.mp4.
upload_file { "path": "~/clips/intro.mp4" }
// → { "downloadUrl": "https://cdn.rendobar.com/u/abc123/intro.mp4", "sizeBytes": 4821004 }
submit_job { "type": "ffmpeg", "inputs": { "intro.mp4": "https://cdn.rendobar.com/u/abc123/intro.mp4" }, "params": { "command": "-i intro.mp4 -af \"volume=enable='lt(t,3)':volume=0\" -c:v copy out.mp4" } } // → { "jobId": "job_9f2a", "status": "waiting" }
get_job { "jobId": "job_9f2a", "wait": true } // → complete · $0.01 · https://cdn.rendobar.com/o/job_9f2a/out.mp4
The agent writes the filter. Rendobar runs it. Nothing gets installed on your machine, and -c:v copy means the video stream is never re-encoded.
Hit a size budget.
You: Get
demo.movunder 25 MB so I can email it.
upload_file { "path": "~/recordings/demo.mov" }
// → { "downloadUrl": "https://cdn.rendobar.com/u/7c1e/demo.mov", "sizeBytes": 251658240 }
submit_job { "type": "compress.target", "inputs": { "source": "https://cdn.rendobar.com/u/7c1e/demo.mov" }, "params": { "for": "web", "target": { "maxSize": "25MB" } } } // → { "jobId": "job_4b8d", "status": "waiting" }
get_job { "jobId": "job_4b8d", "wait": true } // → complete · https://cdn.rendobar.com/o/job_4b8d/out.mp4 · 23.8 MB
You give it the ceiling, not a bitrate. The encoder searches candidate encodes and returns the smallest file that still clears the quality bar, so you are not guessing at CRF values to land under a mail server's limit.
Generate an image.
You: Make a 1920x1080 title card for a video about deep sea diving.
submit_job { "type": "image.generate",
"inputs": {}, "params": { "model": "standard", "prompt": "Title card for a deep sea diving documentary. Shafts of light through deep blue water, small diver silhouette, empty space across the upper third for a title.", "width": 1920, "height": 1080 } } // → { "jobId": "job_2fa7", "status": "waiting" }
get_job { "jobId": "job_2fa7", "wait": true } // → complete · https://cdn.rendobar.com/o/job_2fa7/out.png
inputs is empty because nothing is being transformed. Ask for a tier (economy, standard, premium) and the platform picks the model, or pin an exact model id to reach its own controls. Requested dimensions are snapped to what the chosen model can actually render.
Six more tools, and the prompts that reach them.
You: What can Rendobar actually do?
list_job_types {}
// → { "jobTypes": [ { "type": "compose", "tag": "Compose", // "summary": "Render a video from a declarative JSON timeline", // "acceptsMedia": ["video", "image", "audio"] }, ... ], // "guidance": "..." }
Read live from the job registry on every call, which is why nothing in this README enumerates job types. A new one appears here without a release.
You: How much credit is left?
get_account {}
// → { "balance": "$4.86", "balanceUsd": 4.86, "plan": "free", "isPro": false, // "limits": { "concurrentJobs": 1, "maxFileSize": "500 MB", "jobTimeoutMin": 60 } }
Worth a call before submitting something expensive.
You: What did I run this morning?
list_jobs { "status": "complete", "limit": 5 }
// → { "jobs": [ { "id": "job_9f2a", "type": "ffmpeg", "status": "complete", // "createdAt": "2026-08-04T09:12:00Z", "cost": "$0.01", // "output": { "url": "https://cdn.rendobar.com/o/job_9f2a/out.mp4" } } ] }
The compact row is enough to find a result you lost. Call get_job when you need the full output.
You: Stop that one, I picked the wrong file.
cancel_job { "jobId": "job_9f2a" }
// → { "id": "job_9f2a", "status": "cancelled" }
Works on waiting, dispatched and running jobs. A running job's upstream execution is stopped too, so you are not billed for work you cancelled.
You: Where can this land once it's done?
list_storage {}
// → { "storage": [ { "id": "prod-media", "provider": "s3", "bucket": "acme-prod-media", // "access": "deliver", "defaultDestination": true, "pending": false } ], // "note": "..." }
The buckets connected on the Storage page, without the endpoint or region an agent has no use for. Use the id in destinations, or as storage:// / in any input.
You: What's already in the archive bucket?
list_storage_files { "storageId": "raw-archive" }
// → { "folders": [ { "prefix": "2026/", "uri": "storage://raw-archive/2026/" } ], // "files": [ { "key": "notes.txt", "size": 800, "lastModified": "2026-09-01T00:00:00.000Z", // "uri": "storage://raw-archive/notes.txt" } ], // "cursor": null }
Every entry carries its own storage:// uri, ready to hand straight to submit_job. Pass a folder's prefix to go one level deeper.
Rendobar has two MCP servers. Pick by whether the agent needs your filesystem.
@rendobar/mcp (this package) | Hosted (api.rendobar.com/mcp) | |
|---|---|---|
| Transport | stdio, spawned by your client | Streamable HTTP |
| Reads local files | Yes. That is the reason it exists | No. The server has no disk |
| Auth | API key | OAuth in the browser, or a Bearer key |
| Best for | Claude Desktop, Cursor, Cline, Zed | claude.ai, ChatGPT, hosted gateways |
Hosted, no API key, one command:
claude mcp add --transport http rendobar https://api.rendobar.com/mcp
Local, for filesystem access. Get a key at app.rendobar.com → Settings → API Keys, then:
claude mcp add rendobar -s user --env RENDOBAR_API_KEY=rb_... -- npx -y @rendobar/mcp
Already ran rb login with the Rendobar CLI? Drop --env. The server finds the credentials file.
Claude Desktop, Cursor, Cline, Windsurf
Same block for all four. Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows). Cursor: ~/.cursor/mcp.json on every OS. Windsurf: ~/.codeium/windsurf/mcp_config.json on every OS. Cline: MCP panel → Configure.
On Linux, use Cursor, Windsurf, Cline, Zed, VS Code or Continue. Claude Desktop has no Linux build, so it is the one client on this list you cannot use there. The server itself runs fine on Linux.
{
"mcpServers": { "rendobar": { "command": "npx", "args": ["-y", "@rendobar/mcp"], "env": { "RENDOBAR_API_KEY": "rb_..." } } } }
Restart the client afterwards.
Zed, VS Code, Continue
Zed uses context_servers instead of mcpServers, in ~/.config/zed/settings.json:
{
"context_servers": { "rendobar": { "source": "custom", "command": "npx", "args": ["-y", "@rendobar/mcp"], "env": { "RENDOBAR_API_KEY": "rb_..." } } } }
VS Code 1.101+, in .vscode/mcp.json, prompts for the key instead of storing it:
{
"servers": { "rendobar": { "command": "npx", "args": ["-y", "@rendobar/mcp"], "env": { "RENDOBAR_API_KEY": "${input:rendobarKey}" } } }, "inputs": [{ "id": "rendobarKey", "type": "promptString", "password": true, "description": "Rendobar API Key" }] }
Continue, in .continue/mcpServers/rendobar.yaml:
type: stdio
command: npx args: ["-y", "@rendobar/mcp"] env: RENDOBAR_API_KEY: rb_...
</details>
Runs on macOS, Linux and Windows. Every release is tested on all three in CI. There are no native dependencies, so architecture does not matter: x64 and arm64 both work. Needs Node 20.10 or later, and the server checks at startup and exits with a clear message on older versions.
| Tool | Purpose |
|---|---|
upload_file | Upload a local file. Returns a URL to use in submit_job. |
list_job_types | Every active job type, read live. Call this first. |
submit_job | Submit a job of any type. |
get_job | Status and result. Pass wait: true to long-poll for ~50s. |
list_jobs | Recent jobs. |
cancel_job | Cancel a waiting, dispatched or running job. |
get_account | Balance, plan limits, active job count. |
list_storage | Buckets connected on the Storage page, for storage:// inputs and destinations. |
list_storage_files | Folders and files in a connected bucket, each with its storage:// URI. |
**`ffmp
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