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PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Ffmpeg Micro MCP

作者:ffmpeg-micro版本 0.4.3i★ 1i电脑或云端
注册名称com.ffmpeg-micro/mcp-server

安装方法

What it does

MCP server for the FFmpeg Micro video transcoding API — create, monitor, download transcodes.

  • Connects 电脑或云端
  • Account or key Cloud: May need a key; not confirmed (our check was refused) · Local: Needs FFMPEG_MICRO_API_KEY
  • Category 设计、图片与音频

安装

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

  1. 打开 Claude 的 Connectors 页面

    CustomizeConnectors

    CustomizeConnectors1+ Add2https://…3Add4
  2. 点击“+ Add” → “Add custom connector”,填写这 2 项

    把这里的内容复制到 Claude 中同名的输入框,然后点击 Continue

    Name · 名称Ffmpeg Micro MCP
    Remote MCP server URL · 地址https://mcp.ffmpeg-micro.com

    Authentication 保持 Claude 检测到的设置,然后点击 Add

  3. 在聊天中开启

    +ConnectorsFfmpeg Micro MCP

  4. 试一试

    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

  1. 在网页版 ChatGPT 打开添加 MCP 的页面

    Plugins+Add custom MCP server

    ChatGPTPlugins1+2https://…3Create4
  2. 填写名称和地址

    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

    Name · 名称Ffmpeg Micro MCP
    Connection · 地址https://mcp.ffmpeg-micro.com
  3. 在聊天中选择 Ffmpeg Micro MCP

    开启新聊天,输入前先从插件列表中选择 Ffmpeg Micro MCP

  4. 试一试

    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

  1. 打开终端

    Mac:按 Command+空格,输入 Terminal · Windows:打开 PowerShell

  2. 粘贴这条命令并按 Enter

    claude mcp add --transport http --scope user ffmpeg-micro https://mcp.ffmpeg-micro.com
  3. 在 Claude Code 中输入 /mcp

    应能看到 Ffmpeg Micro MCP 在列表中并显示已连接

  4. 试一试

    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

  1. 点击这个按钮打开 Cursor

    在 Cursor 中打开

    Cursor 会请你确认安装,点击 Install

  2. 在 Cursor 中以 Agent 模式打开 AI 聊天

  3. 试一试

    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

  1. 点击这个按钮打开 VS Code

    在 VS Code 中打开

    VS Code 会请你确认安装,点击 Install

  2. 在 VS Code 中以 Agent 模式打开 AI 聊天

  3. 试一试

    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

查看所有支持 MCP 的应用 →

通过网页连接的应用使用的地址

地址https://mcp.ffmpeg-micro.com

JSON 设置在下方“开发者专用”中

卡住了?问问正在用 Ffmpeg Micro MCP 的人💬 讨论 →
开发者专用:终端命令和 JSON 设置

通过网页连接

Claude Code

claude mcp add --transport http --scope user ffmpeg-micro https://mcp.ffmpeg-micro.com

Cursor (~/.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"
    }
  }
}

安装在电脑上

Claude Code

claude mcp add --scope user ffmpeg-micro --env FFMPEG_MICRO_API_KEY=your-value -- npx -y @ffmpeg-micro/mcp-server

Claude 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"
      }
    }
  }
}

需要你填写的值

  • FFMPEG_MICRO_API_KEY 机密 必填 env
    API key from https://www.ffmpeg-micro.com/dashboard/api-keys

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。

一览

每周下载量i
82/周
已验证域名
✓ ffmpeg-micro.com
连接方式
电脑或云端
传输方式
stdio, streamable-http
最新版本i
0.4.3
发布到注册中心i
2026年10月17日
代码最后更新i
2026年10月17日
作者
ffmpeg-micro
软件包
npm: @ffmpeg-micro/mcp-server
注册状态
活跃

哪些应用可以使用它

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

全部应用及其优缺点 →

它能做什么

服务器要求先登录,才会列出它的工具。

基础检查i

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

  • ✓ 通过

    作者与仓库一致

    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)

  • ✓ 通过

    仍在维护

    代码最后更新于26天前

  • ✓ 通过

    有许可证

    使用 MIT

  • ! 注意

    端点有响应

    拒绝访问(HTTP 401),且未提供 OAuth 登录。可能需要密钥,也可能是拦截机器人,尚未确认。

  • ! 注意

    使用 OAuth 登录

    需要你自己复制粘贴密钥或令牌

  • … 尚未检查

    工具描述中没有隐藏指令

    尚未检查;我们会在检查端点时读取工具列表

讨论

提问、分享技巧或报告无法使用。按“有帮助”数量排序。

还没有帖子,欢迎第一个提问或分享。

此处内容由用户撰写,不代表 PopNx 团队。

使用 Google 登录后发帖

用户评价

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用 Google 登录后写评价

与“设计、图片与音频”中的其他服务器比较i

  • 星标数多于此分类中其他 2959 个服务器的 78%
  • 发布到注册中心的时间比此分类中其他 2959 个服务器的 53% 更新
  • 基础检查结果等于或优于此分类中其他 2959 个服务器的 55%

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。

常见问题

Ffmpeg Micro MCP 安全吗?

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。

它需要哪些密钥或代码?

你需要填写:FFMPEG_MICRO_API_KEY

它在我的电脑上运行还是在云端?

两种方式都可以:安装在你的电脑上,或者连接云端版本。

它还在维护吗?

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。

哪些应用可以使用它?

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。

有什么可以替代它?

同一分类中有:Figma Context MCP, AutoClip, Next AI Draw.io

同类别的其他选择

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

Figma Context MCP

Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.

作者:GLips广泛使用↓ 15.2万/周★ 1.6万5个月前更新

在你的电脑上运行

AutoClip

Clip long videos into short clips with covers and captions. Local CLI and MCP.

作者:zhouxiaoka广泛使用↓ 205/周★ 1万15天前更新

在你的电脑上运行

Next AI Draw.io

draw.io diagrams your AI agent draws on a live browser canvas that you can also edit by hand

作者:DayuanJiang广泛使用↓ 2.2万/周★ 3.7万 整个仓库的星标5天前更新

在你的电脑上运行

作者的 README

打开 README

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。

@ffmpeg-micro/mcp-server

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.

What it does

Exposes tools that map onto FFmpeg Micro's public API:

ToolWhat it does
transcode_videoCreate a transcode job from one or more input videos (gs:// or https://). Supports quality/resolution presets and raw FFmpeg options.
get_transcodeFetch the current state of a single job.
list_transcodesList jobs with optional status, page, limit, since, until filters.
cancel_transcodeCancel a pending or processing job.
get_download_urlGenerate a 10-minute signed HTTPS URL for a completed job's output file.
transcode_and_waitConvenience: create a job, poll until it finishes, return the signed download URL in one call.
request_upload_urlStep 1 of the direct-upload flow. Returns a presigned HTTPS URL that the host PUTs the file bytes to.
confirm_uploadStep 2 of the direct-upload flow. Returns the final gs:// URL plus probe metadata, ready to use as a transcode/transcribe input.
run_blueprintStart a blueprint run — a pre-built video workflow (captioning, resizing, watermarking, ads, and more).
get_blueprint_runFetch a blueprint run's status, step, and output URLs (multi-output blueprints return labeled outputs).
run_blueprint_and_waitConvenience: start a blueprint run and poll until it completes, fails, or pauses for transcript review.
continue_blueprint_runResume a caption-video run paused in awaiting_review by submitting the approved SRT transcript.
Blueprints

Blueprints are pre-built workflows behind POST /v1/blueprints/{slug}/runs. The tool descriptions document each blueprint's input fields. Notes:

  • Most blueprints run on the FFmpeg lane and meter plan compute minutes (no tokens). Generative blueprints (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.
  • Multi-output blueprints (listing-kit, hook-variants) return an outputs array of {label, url} — prefer it over output_url when present.
  • Output URLs are signed with a 10-minute TTL; re-fetch the run for fresh links.
Uploading a local file

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:

  1. Host calls request_upload_url with {filename, contentType, fileSize} → receives a short-lived presigned HTTPS URL.
  2. Host PUTs the file bytes to that URL with the same Content-Type.
  3. Host calls confirm_upload with {filename: , fileSize} → receives the final gs://... fileUrl.
  4. Host passes that fileUrl to transcribe_audio / transcode_video / transcode_and_wait.

Quick start

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.

Authentication

OAuth (recommended)

The MCP server supports OAuth 2.1 with PKCE and dynamic client registration. Your MCP client handles the entire flow automatically:

  1. Client discovers OAuth endpoints via /.well-known/oauth-authorization-server
  2. Client registers itself dynamically
  3. Browser opens for you to sign in and approve access
  4. Token is exchanged and cached — subsequent connections are instant

This is the default when you use the config above with no headers or env block.

API key (alternative)

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.

stdio (local install)

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.

Compatible tools

The HTTP config (OAuth) works with any MCP client that supports streamable HTTP transport:

  • Claude Code (CLI)
  • Claude Desktop
  • Cursor
  • Windsurf
  • VS Code (GitHub Copilot MCP)

The stdio config works with any MCP client that supports stdio transport.

Example prompts

Once connected, you can ask things like:

  • "Transcode this video to 720p MP4 and give me the download URL when it's done."
  • "Crop this landscape video to a square."
  • "Add a text overlay saying 'Episode 12' to my video."
  • "List my failed jobs from this week."
  • "Cancel job b5f5a9c0-9e33-4e77-8a5b-6a0c2cd9c0b3."

Development

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
Running integration tests locally
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).

Smoke-testing the upload tools end-to-end

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

2. HTTP (any deployed server — local 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

Hitting protection-protected Vercel previews

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.

Release process

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.

Contributor flow (every PR)

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):

  • Add the no-changeset label to the PR, or
  • npx changeset --empty to explicitly declare "no release needed."
Maintainer flow (cutting a release)

You don't manually cut releases. The pipeline does it:

  1. PRs land on `main` with changeset files attached.
  2. `.github/workflows/release.yml` runs on every push to main. When pending changesets exist, it opens (or updates) a chore(release): version packages PR authored by the action. That PR:
  • Runs changeset version to consume the pending changesets
  • Bumps package.json
  • Re-syncs server.json and src/version.ts via scripts/sync-server-version.mjs
  • Appends entries to CHANGELOG.md
  • Commits the result to its own branch
  1. Review and merge the Version Packages PR when you're ready to ship. You can let several changesets accumulate before merging — the PR updates itself as more land on main.
  2. On merge, the release workflow runs again. This time there are no pending changesets, so 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
  • Creates the GitHub Release + git

只显示了开头部分;其余内容请在仓库中阅读