Qwen Max MCP Server

What does “Qwen Max” MCP Server do?

The Qwen Max MCP Server is an implementation of the Model Context Protocol (MCP) designed to connect the Qwen Max language model with external clients, such as AI assistants and development tools. By acting as a bridge, the server enables seamless integration of the Qwen series models into workflows that require advanced language understanding and generation. It enhances development by enabling tasks like large-context inference, multi-step reasoning, and complex prompt interactions. Built on Node.js/TypeScript for maximal stability and compatibility, the server is particularly suitable for use with Claude Desktop and supports secure, scalable deployments. With support for several Qwen model variants, it optimizes for both performance and cost, making it a versatile solution for projects requiring robust language model capabilities.

List of Prompts

No explicit prompt templates are mentioned or described in the repository.

List of Resources

No explicit MCP resource primitives are documented in the repository.

List of Tools

No explicit tools or “server.py” (or equivalent file listing executable tools) are present or described in the repository.

Use Cases of this MCP Server

• Large-context Chat and Inference: Enables applications to interact with the Qwen Max model, which supports up to a 32,768 token context window, ideal for document summarization, code analysis, or multi-step task reasoning.
• Model Experimentation and Evaluation: Developers can benchmark and experiment with different Qwen series models (Max, Plus, Turbo) via a unified MCP interface to select the best fit for their use case.
• Seamless Integration with Claude Desktop: The server is designed for out-of-the-box compatibility with Claude Desktop, providing a stable and reliable workflow for AI-powered productivity.
• API-based Language Model Access: Allows developers to securely expose Qwen model capabilities as a service, suitable for building chatbots, assistants, or automation scripts that need robust language understanding.
• Token Cost Management: With clear documentation of pricing and free quota, organizations can efficiently manage their token consumption for large-scale deployments.

How to set it up

Windsurf

1. Ensure Node.js (v18+) and npm are installed.
2. Install the MCP server package:

   npx -y @smithery/cli install @66julienmartin/mcp-server-qwen_max --client windsurf
   

3. Locate your Windsurf configuration file and add the MCP server configuration:

   {
     "mcpServers": [
       {
         "command": "npx",
         "args": ["@66julienmartin/mcp-server-qwen_max", "start"]
       }
     ]
   }
   

4. Save the config and restart Windsurf.
5. Verify the server appears in the Windsurf UI.

Securing API Keys

{
  "env": {
    "DASHSCOPE_API_KEY": "<your_api_key>"
  }
}

Claude

1. Install Node.js (v18+) and npm.
2. Use Smithery to install for Claude Desktop:

   npx -y @smithery/cli install @66julienmartin/mcp-server-qwen_max --client claude
   

3. Edit your Claude Desktop configuration to include:

   {
     "mcpServers": [
       {
         "command": "npx",
         "args": ["@66julienmartin/mcp-server-qwen_max", "start"]
       }
     ]
   }
   

4. Restart Claude Desktop.
5. Confirm the MCP server is running.

Securing API Keys

{
  "env": {
    "DASHSCOPE_API_KEY": "<your_api_key>"
  }
}

Cursor

1. Install Node.js and npm.
2. From your terminal:

   npx -y @smithery/cli install @66julienmartin/mcp-server-qwen_max --client cursor
   

3. Update Cursor’s configuration:

   {
     "mcpServers": [
       {
         "command": "npx",
         "args": ["@66julienmartin/mcp-server-qwen_max", "start"]
       }
     ]
   }
   

4. Restart Cursor.
5. Check that the server is listed.

Securing API Keys

{
  "env": {
    "DASHSCOPE_API_KEY": "<your_api_key>"
  }
}

Cline

1. Install Node.js and npm.
2. Run the install command:

   npx -y @smithery/cli install @66julienmartin/mcp-server-qwen_max --client cline
   

3. Add the server to your Cline config file:

   {
     "mcpServers": [
       {
         "command": "npx",
         "args": ["@66julienmartin/mcp-server-qwen_max", "start"]
       }
     ]
   }
   

4. Save and restart Cline.
5. Ensure the MCP server is operational in Cline.

Securing API Keys

{
  "env": {
    "DASHSCOPE_API_KEY": "<your_api_key>"
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "qwen-max": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “qwen-max” to whatever the actual name of your MCP server is (e.g., “github-mcp”, “weather-api”, etc.) and replace the URL with your own MCP server URL.

Overview

Based on the information provided, the Qwen Max MCP Server is well-documented for installation and model details but lacks explicit documentation or implementation of MCP resources, tools, or prompt templates in the public repository. This limits its extensibility and out-of-the-box utility for advanced MCP features.

Our opinion

We would rate this MCP server a 5/10. While its installation and model support are clear and the project is open source with a permissive license, the lack of documented tools, resources, and prompt templates reduces its immediate value for workflows that depend on MCP’s full capabilities.

MCP Score