3dify-project/dify-mcp-client

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.