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Qwen Max MCP Server

by kamelirzouni
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JavaScript
Hybrid

Qwen Max MCP Server

A Model Context Protocol (MCP) server implementation for the Qwen Max language model.

Why Node.js? This implementation uses Node.js/TypeScript as it currently provides the most stable and reliable integration with MCP servers compared to other languages like Python. The Node.js SDK for MCP offers better type safety, error handling, and compatibility with Claude Desktop.

Prerequisites

  • Node.js (v18 or higher)

  • npm

  • Claude Desktop

  • Dashscope API key

Related MCP server: DeepSeek MCP Server

Installation

Installing via Smithery

To install Qwen Max MCP Server for Claude Desktop automatically via Smithery:

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

Manual Installation

git clone https://github.com/66julienmartin/mcp-server-qwen-max.git
cd Qwen_Max
npm install

Model Selection

By default, this server uses the Qwen-Max model. The Qwen series offers several commercial models with different capabilities:

Qwen-Max

Provides the best inference performance, especially for complex and multi-step tasks.

Context window: 32,768 tokens

  • Max input: 30,720 tokens

  • Max output: 8,192 tokens

  • Pricing: $0.0016/1K tokens (input), $0.0064/1K tokens (output)

  • Free quota: 1 million tokens

Available versions:

  • qwen-max (Stable)

  • qwen-max-latest (Latest)

  • qwen-max-2025-01-25 (Snapshot, also known as qwen-max-0125 or Qwen2.5-Max)

Qwen-Plus

Balanced combination of performance, speed, and cost, ideal for moderately complex tasks.

Context window: 131,072 tokens

  • Max input: 129,024 tokens

  • Max output: 8,192 tokens

  • Pricing: $0.0004/1K tokens (input), $0.0012/1K tokens (output)

  • Free quota: 1 million tokens

Available versions:

  • qwen-plus (Stable)

  • qwen-plus-latest (Latest)

  • qwen-plus-2025-01-25 (Snapshot, also known as qwen-plus-0125)

Qwen-Turbo

Fast speed and low cost, suitable for simple tasks.

  • Context window: 1,000,000 tokens

  • Max input: 1,000,000 tokens

  • Max output: 8,192 tokens

  • Pricing: $0.00005/1K tokens (input), $0.0002/1K tokens (output)

  • Free quota: 1 million tokens

Available versions:

  • qwen-turbo (Stable)

  • qwen-turbo-latest (Latest)

  • qwen-turbo-2024-11-01 (Snapshot, also known as qwen-turbo-1101)

To modify the model, update the model name in src/index.ts:

// For Qwen-Max (default)
model: "qwen-max"

// For Qwen-Plus
model: "qwen-plus"

// For Qwen-Turbo
model: "qwen-turbo"

For more detailed information about available models, visit the Alibaba Cloud Model Documentation https://www.alibabacloud.com/help/en/model-studio/getting-started/models?spm=a3c0i.23458820.2359477120.1.446c7d3f9LT0FY.

Project Structure

qwen-max-mcp/
├── src/
│   ├── index.ts             # Main server implementation
├── build/                   # Compiled files
│   ├── index.js
├── LICENSE
├── README.md
├── package.json
├── package-lock.json
└── tsconfig.json

Configuration

  1. Create a .env file in the project root:

DASHSCOPE_API_KEY=your-api-key-here

  1. Update Claude Desktop configuration:

{
  "mcpServers": {
    "qwen_max": {
      "command": "node",
      "args": ["/path/to/Qwen_Max/build/index.js"],
      "env": {
        "DASHSCOPE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Development

npm run dev     # Watch mode
npm run build   # Build
npm run start   # Start server

Features

  • Text generation with Qwen models

  • Configurable parameters (max_tokens, temperature)

  • Error handling

  • MCP protocol support

  • Claude Desktop integration

  • Support for all Qwen commercial models (Max, Plus, Turbo)

  • Extensive token context windows

API Usage

// Example tool call
{
  "name": "qwen_max",
  "arguments": {
    "prompt": "Your prompt here",
    "max_tokens": 8192,
    "temperature": 0.7
  }
}

The Temperature Parameter

The temperature parameter controls the randomness of the model's output:

Lower values (0.0-0.7): More focused and deterministic outputs Higher values (0.7-1.0): More creative and varied outputs

Recommended temperature settings by task:

Code generation: 0.0-0.3 Technical writing: 0.3-0.5 General tasks: 0.7 (default) Creative writing: 0.8-1.0

Error Handling

The server provides detailed error messages for common issues:

API authentication errors Invalid parameters Rate limiting Network issues Token limit exceeded Model availability issues

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT

Available Tools

1 tool
qwen_maxC

Generate text using Qwen Max model

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesThe text prompt to generate content from
max_tokensNoMaximum number of tokens to generate
temperatureNoSampling temperature (0-2)

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden for behavioral disclosure. While 'Generate text' implies a read-only operation, it doesn't disclose important behavioral traits like rate limits, authentication requirements, response format, error conditions, or cost implications. For a text generation tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise at just 5 words. Every word earns its place by specifying the action ('Generate'), resource ('text'), and model ('Qwen Max model'). There's no wasted language, repetition, or unnecessary elaboration. The structure is front-loaded with the core function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given this is a text generation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, error conditions, rate limits, or any behavioral characteristics. While the schema covers parameters well, the overall context for using this tool effectively is incomplete. A text generation tool needs more contextual information than provided.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all parameters are documented in the schema. The description adds no parameter-specific information beyond what the schema already provides. It doesn't explain relationships between parameters, provide examples, or add semantic context. The baseline score of 3 reflects adequate parameter documentation coming entirely from the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Generate text using Qwen Max model' - a specific verb ('Generate') with resource ('text') and model specification. It distinguishes itself as a text generation tool, though with no sibling tools, differentiation isn't needed. The purpose is unambiguous but could be slightly more specific about the type of text generation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With no sibling tools mentioned, there's no context about other available models or tools. It doesn't mention prerequisites, limitations, or ideal use cases. The agent receives only the basic function without usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.2/5.0
Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'qwen_max' has a clear and distinct purpose: generating text using the Qwen Max model.

Naming Consistency5/5

Since there is only one tool, naming consistency is inherently perfect. The tool name 'qwen_max' follows a single, consistent pattern with no deviations or mixing of conventions to evaluate.

Tool Count2/5

A single tool is too few for most server purposes, as it severely limits functionality and scope. While it might suffice for a minimal text generation service, it lacks the breadth typically expected for an MCP server, making it feel thin and under-scoped.

Completeness3/5

The tool provides a basic text generation capability, but there are notable gaps for a comprehensive AI model server. For example, it lacks tools for managing models, handling different input formats, or performing other common AI tasks like classification or summarization, which limits its utility.

Maintenance

ActivityInactive
ResponsivenessNo issues

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