# 通义万相 MCP 服务器 这是一个基于 TypeScript 的 Model Context Protocol (MCP) 服务器，专门提供阿里云通义万相的文生图(Text-to-Image)和文生视频(Text-to-Video)能力。该服务器通过 MCP
Everything the assistant can do here goes through one of these:
model — 模型名称，默认为 wanx2.1-t2i-turbosize — 图像尺寸，默认为 1024*1024seed — 随机种子，用于复现结果prompt_extend — 是否启用提示词扩展，默认为 truewatermark — 是否添加水印，默认为 falseYou will need one environment variable: DASHSCOPE_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. 通义万相 MCP 服务器's toolset — model, size, seed and 2 more — is a fair guide to whether it matches your workflow. It is maintained by Suixinlei; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| model | 模型名称，默认为 wanx2.1-t2i-turbo |
| size | 图像尺寸，默认为 1024*1024 |
| seed | 随机种子，用于复现结果 |
| prompt_extend | 是否启用提示词扩展，默认为 true |
| watermark | 是否添加水印，默认为 false |
{
"mcpServers": {
"tongyi-wanxiang": {
"command": "npx",
"args": [
"-y",
"tongyi-wanx-mcp-server@latest"
],
"env": {
"DASHSCOPE_API_KEY": "<你的通义万相 API 密钥>"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| DASHSCOPE_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.