3
3dify-project/dify-mcp-client
Dify plugin that acts as an MCP client
在 GitHub 查看 ↗链接已于 2026-11-12 核查 · HTTP 200 · 打开的是厂商自己的官网
安装与配置
客户端配置（取自该仓库的安装命令）
{
"mcpServers": {
"3dify-project-dify-mcp-client": {
"command": "uv",
"args": ["tool", "install", "mcp-proxy"]
}
}
}解析自我们于 2026-11-07 抓取的该仓库 README；我们未执行过。
command/args 来自仓库写明的安装命令；不确定时请以该项目 README 为准。
VS Code（.vscode/mcp.json —— 键名是 servers）
{
"servers": {
"3dify-project-dify-mcp-client": {
"command": "uv",
"args": ["tool", "install", "mcp-proxy"]
}
}
}VS Code 顶层键是 <code>servers</code>，其余与上面那份相同。
Claude Code（命令行）
claude mcp add --transport stdio 3dify-project-dify-mcp-client -- uv tool install mcp-proxy
Claude Code 不收 JSON 文件，用这条命令直接加。
配置项的键名以你的客户端为准：客户端与 CLI
- 来源明细
- 收录自 abordage/awesome-mcp 榜单（我们从中收集的条目之一） · 条目类型：MCP 服务
- 收录与活跃
- 收录于 2026-11-05 · 最近见到 2026-11-14 · 共见到 10 次 · 被 10 个来源列出
- 评分构成
- 综合分 1,003 · 10 个来源 · mcp
- 安装
uv tool install mcp-proxy
常见问题（答案都来自我们的记录）
怎么安装？
该仓库给出的安装命令是
uv tool install mcp-proxy。我们的记录里它是仓库自己提供的；不保证在你的客户端上直接可用。它适合谁 / 用来做什么？
我们把它归在这些标签下: MCP 服务。标签来自条目自身，不是我们强加的分类。
谁维护它、还在更新吗？
我们收录于 2026-11-05；最近见到 2026-11-14。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
# dify-mcp-client
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
MCP Client as Agent Strategy Plugin with Computer Using Agent (UI-TARS-SDK) support.
> [!IMPORTANT]
> Dify is not MCP Server but MCP Host.
## How it works
Each MCP client (ReAct Agent) node can connect MCP servers.
1. Tool, Resource, Prompt lists are converted into Dify Tools.
2. Your selected LLM can see their name, description, argument type
3. The LLM calls Tools based on the ReAct loop (Reason → Act → Observe).
> [!NOTE]
> Most of the code in this repository contains the following files.
> #### Dify Official Plugins / Agent Strategies
> https://github.com/langgenius/dify-official-plugins/tree/main/agent-strategies/cot_agent
## ✅ What I did
Copied ReAct.py and renamed file as mcpReAct.py
Added config_json GUI input field by editing mcpReAct.yaml and class mcpReActParams()
### in mcpReAct.py, I added
New 12 functions for MCP
__init__() for initializing AsyncExitStack and event loop
Some codes in _handle_invoke_action() for MCP
MCP setup and cleanup in _invoke()
> [!IMPORTANT]
> ReAct while loop is as they are
## 🔄 Update history
Add SSE MCP client (v0.0.2)
Support multi SSE servers (v0.0.3)
Update python module and simplify its dependency (v0.0.4)
mcp(v1.1.2→v1.6.0+)
dify_plugin(0.0.1b72→v0.1.0)
Add UI-TARS SDK integration for GUI automation capabilities (v0.0.5)
Support Streamable HTTP MCP client
Feat SSE param: /sse?key=value (v0.0.6)
## 🤖 UI-TARS Integration
This plugin includes UI-TARS SDK integration for GUI automation capabilities.
> [!WARNING]
> UI-TARS-SDK integration is supported only Dify Plugin's local debug deployment.
> https://github.com/3dify-project/dify-mcp-client#-how-to-develop-and-deploy-plugin
> Normal difypkg install doesn't work. Because UI-TARS require OS native API, yet Dify plugin env is Linux docker container.
> I'm thinking alternative solusion via Streamable HTTP MCP.
### Key Features
On-demand GUI automation: UI-TARS is called only when needed, reducing token consumption
Life-time control: Set maximum loop count per task to prevent runaway automation
### Known Limitations
Single Monitor Support: UI-TARS currently recognizes the primary monitor only. Multi-monitor setups are not supported.
Mac Retina Display Issue: On macOS with Retina displays, UI-TARS requires the display resolution to be set to "Default" instead of the highest quality setting. Otherwise wrong (w,h) point is clicked. https://github.com/bytedance/UI-TARS-desktop/issues/591
### Life-time Parameter
The life_time parameter controls the maximum number of GUI actions UI-TARS can perform:
Default: 10 iterations
User-configurable maximum via ui_tars_max_life_time_count
Your selected LLM can dynamically adjust within the user-defined limit based on task complexity
> [!NOTE]
> Currently hardcoded to use UI-TARS-1.5-7B model for optimal cost-performance balance.
## 🐳 Docker Deployment with Pre-built Node.js
### Building the Docker Image
This pulldown guide is for TypeScript stdio MCP server user
bash
docker build -t dify-mcp-client:latest .
Or use our pre-built image:
yaml
# In your docker-compose.yml
services:
plugin-daemon:
image: memedayo/dify-plugin-daemon:latest # with Pre-built Node.js
# ... rest of configuration
Without Node.js in container, you lose TypeScript stdio MCP support.
> [!IMPORTANT]
> Dify is not MCP Server but MCP Host.
## How it works
Each MCP client (ReAct Agent) node can connect MCP servers.
1. Tool, Resource, Prompt lists are converted into Dify Tools.
2. Your selected LLM can see their name, description, argument type
3. The LLM calls Tools based on the ReAct loop (Reason → Act → Observe).
> [!NOTE]
> Most of the code in this repository contains the following files.
> #### Dify Official Plugins / Agent Strategies
> https://github.com/langgenius/dify-official-plugins/tree/main/agent-strategies/cot_agent
## ✅ What I did
Copied ReAct.py and renamed file as mcpReAct.py
Added config_json GUI input field by editing mcpReAct.yaml and class mcpReActParams()
### in mcpReAct.py, I added
New 12 functions for MCP
__init__() for initializing AsyncExitStack and event loop
Some codes in _handle_invoke_action() for MCP
MCP setup and cleanup in _invoke()
> [!IMPORTANT]
> ReAct while loop is as they are
## 🔄 Update history
Add SSE MCP client (v0.0.2)
Support multi SSE servers (v0.0.3)
Update python module and simplify its dependency (v0.0.4)
mcp(v1.1.2→v1.6.0+)
dify_plugin(0.0.1b72→v0.1.0)
Add UI-TARS SDK integration for GUI automation capabilities (v0.0.5)
Support Streamable HTTP MCP client
Feat SSE param: /sse?key=value (v0.0.6)
## 🤖 UI-TARS Integration
This plugin includes UI-TARS SDK integration for GUI automation capabilities.
> [!WARNING]
> UI-TARS-SDK integration is supported only Dify Plugin's local debug deployment.
> https://github.com/3dify-project/dify-mcp-client#-how-to-develop-and-deploy-plugin
> Normal difypkg install doesn't work. Because UI-TARS require OS native API, yet Dify plugin env is Linux docker container.
> I'm thinking alternative solusion via Streamable HTTP MCP.
### Key Features
On-demand GUI automation: UI-TARS is called only when needed, reducing token consumption
Life-time control: Set maximum loop count per task to prevent runaway automation
### Known Limitations
Single Monitor Support: UI-TARS currently recognizes the primary monitor only. Multi-monitor setups are not supported.
Mac Retina Display Issue: On macOS with Retina displays, UI-TARS requires the display resolution to be set to "Default" instead of the highest quality setting. Otherwise wrong (w,h) point is clicked. https://github.com/bytedance/UI-TARS-desktop/issues/591
### Life-time Parameter
The life_time parameter controls the maximum number of GUI actions UI-TARS can perform:
Default: 10 iterations
User-configurable maximum via ui_tars_max_life_time_count
Your selected LLM can dynamically adjust within the user-defined limit based on task complexity
> [!NOTE]
> Currently hardcoded to use UI-TARS-1.5-7B model for optimal cost-performance balance.
## 🐳 Docker Deployment with Pre-built Node.js
### Building the Docker Image
This pulldown guide is for TypeScript stdio MCP server user
bash
docker build -t dify-mcp-client:latest .
Or use our pre-built image:
yaml
# In your docker-compose.yml
services:
plugin-daemon:
image: memedayo/dify-plugin-daemon:latest # with Pre-built Node.js
# ... rest of configuration
Without Node.js in container, you lose TypeScript stdio MCP support.
收录徽章（可选）
想把收录标记放进 README 就复制这行，可选，不影响收录。
[](https://anclum.com/zh/s/3dify-project-dify-mcp-client/)
- 来源
- awesome · https://github.com/3dify-project/dify-mcp-client
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