Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
MCP server for the FFmpeg Micro video transcoding API — create, monitor, download transcodes.
选择你使用的 AI 应用,然后复制设置。
通过 3/5 项检查 · 你的提问会经过开发者的服务器(mcp.ffmpeg-micro.com)
第一步:你用哪个 AI?
选择你用的应用,步骤会显示在下方。
可连接 Claude · 约 2 分钟
不需要密钥,不用安装程序 · 在 claude.ai 或桌面应用设置一次,即可在聊天中使用 · 免费账户可添加 1 个 · Team/Enterprise 方案由所有者(Owner)添加
CustomizeConnectors
把这里的内容复制到 Claude 中同名的输入框,然后点击 Continue
Ffmpeg Micro MCPhttps://mcp.ffmpeg-micro.comAuthentication 保持 Claude 检测到的设置,然后点击 Add
+ConnectorsFfmpeg Micro MCP
用 Ffmpeg Micro MCP 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Claude 说正在使用 Ffmpeg Micro MCP,或回答中出现 Ffmpeg Micro MCP 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同
仅企业版 ChatGPT 可连接
适用于网页版 ChatGPT Business、Enterprise 或 Edu,需由工作区管理员或所有者添加 · 使用 Free、Plus 或 Pro:你的 ChatGPT 方案暂时无法自行连接 MCP
Plugins+Add custom MCP server
输入名称,在 Connection 下粘贴地址,勾选风险提示,然后点击 Create as a plugin
Ffmpeg Micro MCPhttps://mcp.ffmpeg-micro.com开启新聊天,输入前先从插件列表中选择 Ffmpeg Micro MCP
用 Ffmpeg Micro MCP 告诉我你能帮我做什么,然后做一件给我看✓ 如果 ChatGPT 说正在使用 Ffmpeg Micro MCP,或回答中出现 Ffmpeg Micro MCP 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 OpenAI 帮助页面核实 · 你的界面可能因版本略有不同
可连接 Claude Code · 约 2 分钟
不需要密钥,不用安装程序
Mac:按 Command+空格,输入 Terminal · Windows:打开 PowerShell
claude mcp add --transport http --scope user ffmpeg-micro https://mcp.ffmpeg-micro.com应能看到 Ffmpeg Micro MCP 在列表中并显示已连接
用 Ffmpeg Micro MCP 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Claude Code 说正在使用 Ffmpeg Micro MCP,或回答中出现 Ffmpeg Micro MCP 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同
可连接 Cursor · 约 2 分钟
不需要密钥,不用安装程序
用 Ffmpeg Micro MCP 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Cursor 说正在使用 Ffmpeg Micro MCP,或回答中出现 Ffmpeg Micro MCP 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Cursor 帮助页面核实 · 你的界面可能因版本略有不同
可连接 VS Code · 约 2 分钟
不需要密钥,不用安装程序
用 Ffmpeg Micro MCP 告诉我你能帮我做什么,然后做一件给我看✓ 如果 VS Code 说正在使用 Ffmpeg Micro MCP,或回答中出现 Ffmpeg Micro MCP 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 VS Code 帮助页面核实 · 你的界面可能因版本略有不同
其他支持 MCP 的应用(如 Windsurf、Zed、Gemini CLI)添加方式各不相同,请在应用列表中查看
通过网页连接的应用使用的地址
https://mcp.ffmpeg-micro.comJSON 设置在下方“开发者专用”中
Claude Code
claude mcp add --transport http --scope user ffmpeg-micro https://mcp.ffmpeg-micro.comCursor (~/.cursor/mcp.json)
{
"mcpServers": {
"ffmpeg-micro": {
"url": "https://mcp.ffmpeg-micro.com"
}
}
}VS Code (mcp.json)
{
"servers": {
"ffmpeg-micro": {
"type": "http",
"url": "https://mcp.ffmpeg-micro.com"
}
}
}Claude Code
claude mcp add --scope user ffmpeg-micro --env FFMPEG_MICRO_API_KEY=your-value -- npx -y @ffmpeg-micro/mcp-serverClaude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"ffmpeg-micro": {
"command": "npx",
"args": [
"-y",
"@ffmpeg-micro/mcp-server"
],
"env": {
"FFMPEG_MICRO_API_KEY": "your-value"
}
}
}
}Cursor (~/.cursor/mcp.json)
{
"mcpServers": {
"ffmpeg-micro": {
"command": "npx",
"args": [
"-y",
"@ffmpeg-micro/mcp-server"
],
"env": {
"FFMPEG_MICRO_API_KEY": "your-value"
}
}
}
}VS Code (mcp.json)
{
"servers": {
"ffmpeg-micro": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@ffmpeg-micro/mcp-server"
],
"env": {
"FFMPEG_MICRO_API_KEY": "your-value"
}
}
}
}FFMPEG_MICRO_API_KEY 机密 必填 env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。
依据这个服务器的连接方式和各应用自己的文档,检查于 2026年11月10日。
通过互联网连接,适用于
ClaudeChatGPTGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatOpen WebUIRaycast
在你的电脑上运行,适用于
ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
服务器要求先登录,才会列出它的工具。
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12日
注册名称已通过域名 ffmpeg-micro.com 验证(注册中心会核实域名所有权)
代码最后更新于26天前
使用 MIT
需要你自己复制粘贴密钥或令牌
尚未检查;我们会在检查端点时读取工具列表
提问、分享技巧或报告无法使用。按“有帮助”数量排序。
还没有评价
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 5 项适用的基础检查中通过了 3 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:FFMPEG_MICRO_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26天前,最新版本(0.4.3)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,因此也能用于支持远程 MCP 的应用,例如 ChatGPT Business 及以上方案。
同一分类中有:Figma Context MCP, AutoClip, Next AI Draw.io
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.
draw.io diagrams your AI agent draws on a live browser canvas that you can also edit by hand
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。
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 step