Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
作者提供的说明(英文原文)
Transform video, audio and images, and generate media from prompts. FFmpeg, captions, models.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
RENDOBAR_API_KEY标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
适用于网页版、桌面版和手机版 Claude:
https://api.rendobar.com/mcp打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"rendobar": {
"command": "npx",
"args": [
"-y",
"@rendobar/mcp"
],
"env": {
"RENDOBAR_API_KEY": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add --transport http rendobar https://api.rendobar.com/mcpclaude mcp add rendobar --env RENDOBAR_API_KEY=your-value -- npx -y @rendobar/mcp运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"rendobar": {
"url": "https://api.rendobar.com/mcp"
}
}
}粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"rendobar": {
"command": "npx",
"args": [
"-y",
"@rendobar/mcp"
],
"env": {
"RENDOBAR_API_KEY": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"rendobar": {
"type": "http",
"url": "https://api.rendobar.com/mcp"
}
}
}粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"rendobar": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@rendobar/mcp"
],
"env": {
"RENDOBAR_API_KEY": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
ChatGPT 只能连接互联网上的服务器:
https://api.rendobar.com/mcp菜单名称可能因套餐和版本不同而略有差异。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
我们还没有读取这个服务器的工具列表;我们会轮流检查各个端点。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日
注册名称已通过域名 rendobar.com 验证(注册中心会核实域名所有权)
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
尚未检查;我们会轮流检查各个端点
尚未检查;我们会轮流检查各个端点
尚未检查;我们会在检查端点时读取工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
你需要填写:RENDOBAR_API_KEY
两种方式都可以:安装在你的电脑上,或者连接云端版本。
GitHub 仓库最后更新于–,最新版本(1.13.0)于26天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。
同一分类中有:Figma Context MCP, Claude Real Video, Figma MCP Server
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间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.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
@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
只显示了开头部分;其余内容请在仓库中阅读