原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

WaveSpeed

作者:WaveSpeedAI版本 1.2.10i★ –i电脑或云端
注册名称ai.wavespeed/mcp

安装

概览

作者提供的说明(英文原文)

Run any model on the live WaveSpeed catalog: image, video, audio, 3D.

PopNx MCP 的三大支柱

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。

它能帮你做什么

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。

  • 连接方式: 电脑或云端
  • 传输方式: stdio, Streamable HTTP
  • 分类: 设计、图片与音频
  • 你需要填写:WAVESPEED_API_KEY

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:3 项中通过 3 项
  • 在作者的服务器上运行:你向它提出的内容会经过他们的服务器。
  • 需要你自己填写: WAVESPEED_API_KEY, WAVESPEED_BASE_URL, WAVESPEED_CLIENT_NAME

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

  • 在云端运行无需安装任何东西,通过互联网连接
  • npm已安装 Node.js(用于 npx 命令)

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:

  1. 打开 Settings → Connectors
  2. 点击 Add custom connector
  3. 粘贴下面的地址,然后点击 Add
  4. 如果要求登录,请登录作者的服务
SettingsConnectors1+ Add2https://…3
https://api.wavespeed.ai/mcp

或者在你的电脑上运行

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "wavespeed": {
      "command": "npx",
      "args": [
        "-y",
        "@wavespeed/mcp"
      ],
      "env": {
        "WAVESPEED_API_KEY": "your-value",
        "WAVESPEED_BASE_URL": "your-value",
        "WAVESPEED_CLIENT_NAME": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add --transport http wavespeed https://api.wavespeed.ai/mcp

或者在你的电脑上运行

claude mcp add wavespeed --env WAVESPEED_API_KEY=your-value --env WAVESPEED_BASE_URL=your-value --env WAVESPEED_CLIENT_NAME=your-value -- npx -y @wavespeed/mcp

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "wavespeed": {
      "url": "https://api.wavespeed.ai/mcp"
    }
  }
}

或者在你的电脑上运行

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "wavespeed": {
      "command": "npx",
      "args": [
        "-y",
        "@wavespeed/mcp"
      ],
      "env": {
        "WAVESPEED_API_KEY": "your-value",
        "WAVESPEED_BASE_URL": "your-value",
        "WAVESPEED_CLIENT_NAME": "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。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "wavespeed": {
      "type": "http",
      "url": "https://api.wavespeed.ai/mcp"
    }
  }
}

或者在你的电脑上运行

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "wavespeed": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@wavespeed/mcp"
      ],
      "env": {
        "WAVESPEED_API_KEY": "your-value",
        "WAVESPEED_BASE_URL": "your-value",
        "WAVESPEED_CLIENT_NAME": "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:

  1. 打开网页版 ChatGPT → Settings → Connectors (Apps)
  2. 点击 Create 或 Add(部分套餐需要先在 Advanced 中开启 Developer mode)
  3. 粘贴下面的地址,起个名字,然后保存
  4. 如果要求登录,请登录作者的服务
SettingsConnectors1+ Create2https://…3
https://api.wavespeed.ai/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。

需要你填写的值

  • WAVESPEED_API_KEY 机密 env
    WaveSpeed API key (https://wavespeed.ai/accesskey). Optional when the wavespeed CLI is logged in.
  • WAVESPEED_BASE_URL env
    Override the WaveSpeed API base URL. Defaults to https://api.wavespeed.ai.
  • WAVESPEED_CLIENT_NAME env
    Override the client name reported for channel attribution. Defaults to wavespeed-mcp.

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6日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过域名 wavespeed.ai 验证(注册中心会核实域名所有权)

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • ✓ 通过

    端点有响应

    在 65 毫秒内响应(HTTP 401)

  • ✓ 通过

    使用 OAuth 登录

    声明支持 OAuth 登录,你的 AI 应用可以带你去登录

  • … 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

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 2166 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 2166 个服务器的 100% 更新
  • 基础检查结果等于或优于此分类中其他 2166 个服务器的 100%

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

WaveSpeed 安全吗?

它在 3 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:WAVESPEED_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–,最新版本(1.2.10)于今天进入注册中心。

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,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。

有什么可以替代它?

同一分类中有:Figma Context MCP, Claude Real Video, Figma MCP Server

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ 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

作者:GLips

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

在你的电脑上运行★ 16,165MIT5个月前更新
安装

Claude Real Video

作者:HUANGCHIHHUNGLeo

Let any LLM watch a video locally — and search everything it has ever watched.

在你的电脑上运行★ 2,284MIT2个月前更新
安装

Figma MCP Server

作者:figma

The Figma MCP server brings Figma design context directly into your AI workflow.

在云端运行★ 2,15213个月前更新
安装

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。

WaveSpeed MCP Server

An MCP server for the WaveSpeed platform: run any model on the live catalog — image, video, audio, 3D — from Claude Code, Claude Desktop, Cursor, Cline, or any MCP client.

Built on the same design as the open-source @wavespeed/cli:

  • No hardcoded models for `run_model`. The catalog comes from the live API (1h cache); new platform models work the day they ship.
  • One general verb, five shortcuts. run_model executes anything; get_model_schema tells the agent what inputs a model actually accepts, so it reads before it writes. generate_image, edit_image, generate_video, edit_video and extend_video skip that: they use the same models as the WaveSpeed /chat page, pick the endpoint for what you give them and fit the request to its live schema.
  • Inputs are never mutated. The one explicit transform is the @path marker — a "@./photo.jpg" value inside input uploads the file and substitutes its hosted URL. Bare paths are passed through untouched. The shortcut tools (generate_image etc.) are the exception: they pick the endpoint and fit the request to the model, and report what they changed in notes.
  • Quote before you spend. get_price takes the same input as run_model and returns an estimate; the amount actually charged for a run is authoritative.

Tools

ToolWhat it does
list_modelsSearch the live catalog by text or modality type
get_model_schemaA model's real input schema (required, properties, defaults), with a link to the model's playground page
run_modelSubmit + wait; rejects input keys the model doesn't accept; @path inputs upload automatically; returns output URLs
generate_imageImage from a prompt with the /chat image models (default Seedream 5 Pro); reference images use the model's edit endpoint
edit_imageEdit, restyle or combine existing images with the same models
generate_videoVideo from a prompt, a first/last frame or reference images/videos/audio, with the /chat video models (default Seedance 2.5); the mode (text, image, reference) is picked from the inputs
edit_videoEdit an existing video, optionally with reference images/audio
extend_videoContinue an existing video with new footage
get_priceCost estimate for the inputs you pass (no charge), with a link to the model's playground page
get_balanceAccount credit balance, with the top-up link
list_predictionsRun history (default last 24h); filter by model, status, time range
list_billingsItemized charges and refunds (needs a Billing-access key)
get_usageSpend, request counts and per-model breakdown (default last 7 days; needs a Billing-access key)
delete_predictionsDelete finished runs from the history (up to 100; needs confirm: true, cannot be undone from here)
upload_fileLocal file → hosted URL (24h content-hash dedupe)
get_predictionStatus/outputs of any run by id; optional wait_seconds to wait for it

The five shortcut tools take wait_seconds like run_model, and estimate_only: true returns the price and the exact request that would run without submitting or charging anything. In their prompts, media is named @image 1, @video 1, @audio 1 (in the order given); it is renamed to what each model expects. Models and defaults follow /chat: an unknown model is rejected, and a named model is never switched.

Some generations take minutes to hours. run_model submits the run and returns the prediction id right away (done: false); the assistant then calls get_prediction with that id and wait_seconds (max 1800) until done is true. The task keeps running server-side, so a dropped connection loses nothing. Pass wait_seconds to run_model to wait for a quick model in the same call. While waiting, both tools send MCP progress notifications that carry the prediction id.

Setup

Remote (nothing to install)

The same server is hosted at:

https://api.wavespeed.ai/mcp
  • Claude on the web / ChatGPT: add a custom connector with that address and approve the connection on the WaveSpeed page that opens. Approving creates a dedicated API key (named MCP · ) that you can delete on the API Keys page to disconnect.
  • Claude Code: claude mcp add --transport http wavespeed https://api.wavespeed.ai/mcp
  • Cursor:
  • VS Code: code --add-mcp '{"name":"wavespeed","type":"http","url":"https://api.wavespeed.ai/mcp"}'
  • Clients that only start local commands: connect through mcp-remote, which opens the same browser approval on first start:
  {

"mcpServers": { "wavespeed": { "command": "npx", "args": ["-y", "mcp-remote", "https://api.wavespeed.ai/mcp"] } } }

  • Any other client that sends request headers: point it at the URL with Authorization: Bearer .

The hosted server has the same tools, with one difference for files: it cannot read files on your computer, so pass public URLs, use create_upload from a tool that can send HTTP requests (it returns a temporary upload address and the download_url to use as an input), or, in ChatGPT, attach the file to the chat (the upload_attachment tool, which only ChatGPT can fill, imports it and returns a download_url). Full guide: wavespeed.ai/docs/mcp.

Local (npx)

@latest makes npx fetch the newest release on each start; without it, npx runs a globally installed @wavespeed/mcp if one exists, however old.

Auth resolves from WAVESPEED_API_KEY, or from the CLI's stored login (wavespeed login) — one login covers both tools. Keys: wavespeed.ai/accesskey.

Claude Code

claude mcp add wavespeed -- npx -y @wavespeed/mcp@latest

Claude Desktop / other clients (mcpServers config):

{

"mcpServers": { "wavespeed": { "command": "npx", "args": ["-y", "@wavespeed/mcp@latest"], "env": { "WAVESPEED_API_KEY": "wsk_..." } } } }

Example prompts

  • *"Generate a 16:9 hero image of a cyberpunk skyline at golden hour."*
  • *"Take ./photo.jpg and replace the background with a sunlit kitchen."* (the agent passes "@./photo.jpg")
  • *"Animate ./hero.png with subtle parallax — check the price first."*

Development

npm install

npm run dev # run from source (stdio) npm run lint # typecheck npm run build # tsc → dist/

License

MIT


[WaveSpeed AI](https://wavespeed.ai/) — AI image & video generation platform. Try it in the browser: [Image generator](https://wavespeed.ai/image-generator) · [Video generator](https://wavespeed.ai/video-generator)

问 PopNx