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)
AI video dubbing via OrcaRouter (model orca/dub): upload a video or URL, poll, download the MP4.
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。
ORCADUB_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 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"orcadub": {
"command": "npx",
"args": [
"-y",
"@orcadub/cli"
],
"env": {
"ORCADUB_API_KEY": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add orcadub --env ORCADUB_API_KEY=your-value -- npx -y @orcadub/cli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": {
"orcadub": {
"command": "npx",
"args": [
"-y",
"@orcadub/cli"
],
"env": {
"ORCADUB_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": {
"orcadub": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@orcadub/cli"
],
"env": {
"ORCADUB_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。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
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。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
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8日
注册名称已通过 GitHub 账户 Continuum-AI-Corp 验证,该账户拥有此仓库
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
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
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:ORCADUB_API_KEY
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(1.4.0)于4个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
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:↓ 每周下载量i★ 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。
The official [MCP](https://modelcontextprotocol.io) server for [OrcaDub](https://orcadub.orcarouter.ai) — AI video dubbing, driven from your agent.
Website · API Docs · Get an API key · Config examples · Releases
Give any MCP-capable agent — Claude Code, Claude Desktop, Codex CLI, Cursor, Windsurf — the ability to dub a video into another language: upload a file or pass a URL, submit a job to the orca/dub model through the OrcaRouter gateway, poll progress, and download the finished MP4.
Every operation is a one-shot subcommand (no resident server needed):
export ORCADUB_API_KEY=sk-orca-... # from https://www.orcarouter.ai/console
npx -y @orcadub/cli health npx -y @orcadub/cli upload --path ./clip.mp4 npx -y @orcadub/cli create --source-lang en --target-lang ja \ --url https://youtu.be/... --opt preserve_bgm=true npx -y @orcadub/cli get --video-id npx -y @orcadub/cli download --video-id --dest ./out.mp4
Optional create parameters use repeatable --opt key=val (e.g. --opt watermark=false --opt resolution=1080p --opt glossary.OrcaDub=OrcaDub). Results print as JSON on stdout; errors go to stderr with a non-zero exit.
With no subcommand the same binary runs as an MCP stdio server (npx -y @orcadub/cli).
The dub-video Skill teaches an agent when to dub, which billed parameters it must confirm, and how to run the upload → create → poll → download workflow. Install it interactively:
npx -y @orcadub/cli skill install
The guided installer opens with a balanced 8-row ORCADUB wordmark, then lets you:
Space to toggle, / to filter, a to select all, n to clear all, and Enter to confirm.It supports 36 platforms: the 33-platform Comet-compatible catalog plus Hermes, OpenClaw, and Command Code. Use --lang zh or --lang en to skip the language screen. For unattended or agent-driven setup, select targets explicitly:
# Current project
npx -y @orcadub/cli skill install \ --platform claude --platform codex --scope project --lang en --yes
npx -y @orcadub/cli skill install \ --platform codex --scope global --lang zh --yes
Use --yes to accept detected platforms (or all platforms when none are detected) without opening the installer. Use --json for decoration-free structured output. Set NO_COLOR (or use TERM=dumb) for a color-free installer. Existing identical content is left unchanged; an existing different Skill is preserved unless --force is provided. Skill installation needs network access to the canonical orcadub-plugin repository, but it does not require ORCADUB_API_KEY and does not contact OrcaRouter.
claude mcp add orcadub -e ORCADUB_API_KEY=sk-orca-... -- npx -y @orcadub/cli
Then just ask your agent: *"Dub this into Chinese: https://www.youtube.com/watch?v=…"*
An OrcaRouter API key is required — every tool call is authenticated:
sk-orca-...).ORCADUB_API_KEY environment variable (see below).Dubbing jobs are billed per minute of source video. Without a key the server still starts, but every tool call returns a sign-up redirect to the console.
claude mcp add orcadub -e ORCADUB_API_KEY=sk-orca-... -- npx -y @orcadub/cli
Add to claude_desktop_config.json:
{
"mcpServers": { "orcadub": { "command": "npx", "args": ["-y", "@orcadub/cli"], "env": { "ORCADUB_API_KEY": "sk-orca-..." } } } }
codex mcp add orcadub --env ORCADUB_API_KEY=sk-orca-... -- npx -y @orcadub/cli
Any host that launches stdio MCP servers works with the same shape: command npx, args ["-y", "@orcadub/cli"], env ORCADUB_API_KEY. Ready-to-copy configuration files for Claude Code, Codex, Cursor and Windsurf live in examples/.
Images are published to GHCR for linux/amd64 and linux/arm64:
docker pull ghcr.io/continuum-ai-corp/orcadub-mcp-server:latest
claude mcp add orcadub -e ORCADUB_API_KEY=sk-orca-... -- \ docker run --rm -i -e ORCADUB_API_KEY -v "$PWD:$PWD" -w "$PWD" \ ghcr.io/continuum-ai-corp/orcadub-mcp-server:latest
Or as host JSON config:
{
"mcpServers": { "orcadub": { "command": "docker", "args": ["run", "--rm", "-i", "-e", "ORCADUB_API_KEY", "-v", "/path/to/videos:/work", "-w", "/work", "ghcr.io/continuum-ai-corp/orcadub-mcp-server:latest"], "env": { "ORCADUB_API_KEY": "sk-orca-..." } } } }
Note: dub_upload and dub_download read/write local paths, so mount the directory you upload from / download to (the -v flag) — paths you pass to the tools must be valid inside the container.
Download the binary for your platform from the releases page (verify with checksums.txt), then register it directly:
# example: Linux amd64 via gh CLI
gh release download v0.1.0 -R Continuum-AI-Corp/orcadub-mcp-server \ -p 'orcadub-mcp-server_*_linux_amd64' -O ~/bin/orcadub-mcp-server && chmod +x ~/bin/orcadub-mcp-server claude mcp add orcadub -e ORCADUB_API_KEY=sk-orca-... -- ~/bin/orcadub-mcp-server
git clone https://github.com/Continuum-AI-Corp/orcadub-mcp-server && cd orcadub-mcp-server && make build
| Environment variable | Required | Description |
|---|---|---|
ORCADUB_API_KEY | yes | OrcaRouter sk-orca-... key, sent as Authorization: Bearer on every request. The gateway resolves your workspace and billing from it. |
The gateway address (https://api.orcarouter.ai) is fixed in the binary — there is nothing else to configure.
| Tool | Description | |
|---|---|---|
dub_health | End-to-end probe of gateway → orca/dub routing. No job is created, nothing is billed. | |
dub_upload | Upload a local video file; returns the file_id for dub_create. Files above 64 MiB are chunked automatically (up to 8 GiB). | |
dub_create | Submit a dubbing job (billed). Source is exactly one of file_id or url (YouTube and direct links are fetched server-side). Required: source_lang, target_lang (never auto), and video_name when using file_id. Optional knobs cover content profiles, glossaries, translation style, background-music preservation, lipsync, watermarking, resolution/ratio and more. | |
dub_get | Poll a job: `queued → in_progress → completed \ | failed with integer progress. On completion the response includes content_url (Bearer-authenticated delivery address, never expires) and job_id`. |
dub_download | Save a completed job's MP4 to a local path (refuses to overwrite existing files). |
Supported languages: en zh ja ko fr de es pt ru ar it hi tr th vi id bn pl nl uk fil el cs sv da no fi sk.
A typical agent conversation:
User: Dub this into Chinese: https://www.youtube.com/watch?v=xxxx
Agent: *(dub_create with source_lang=en, target_lang=zh, url=…)* → job queued → *(polls dub_get every ~30 s)* → completed → *(asks: download locally? default: current directory)* → *(dub_download)* → "Saved to ./My-Video-zh.mp4 — re-download any time:
curl -H "Authorization: Bearer sk-orca-..." -o out.mp4"
Direct tool usage from an agent prompt works too — for example dub_create {"source_lang":"en","target_lang":"zh","url":"https://…","glossary":{"OrcaRT":"OrcaDub Realtime"}}.
| Symptom | Cause / fix |
|---|---|
Tool calls return not authorized … www.orcarouter.ai/console | ORCADUB_API_KEY is unset — register, export the key, restart the session. |
401 | Invalid or expired key — re-issue on the OrcaRouter console. |
402 insufficient_credit | Top up your OrcaRouter balance (per-minute billing). |
429 free_quota_exceeded | Free-tier limit reached. |
task_not_exist on dub_get | The id wasn't created through this gateway — use the id exactly as returned by dub_create. |
go build ./...
go test ./... -count=1
Layout: cmd/ (entrypoint) · internal/ (HTTP client, Skill installer, tool layer, wire types) · npm/ (npx launcher that downloads the platform binary from GitHub Releases) · server.json (MCP Registry manifest).
git tag v0.1.0 && git push origin v0.1.0.npm-publish job stamps the tag version into npm/package.json and publishes @orcadub/cli to npm (requires the NPM_TOKEN repo secret).server.json and publishes it to the MCP Registry.MIT