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Qwen Max MCP Server

by kamelirzouni
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JavaScript
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Qwen Max MCP 服务器

Qwen Max 语言模型的模型上下文协议 (MCP) 服务器实现。

铁匠徽章

为什么选择 Node.js？此实现使用 Node.js/TypeScript，因为与 Python 等其他语言相比，它目前能够提供与 MCP 服务器最稳定、最可靠的集成。适用于 MCP 的 Node.js SDK 提供了更好的类型安全性、错误处理能力以及与 Claude Desktop 的兼容性。

先决条件

  • Node.js（v18 或更高版本）

  • npm

  • 克劳德桌面

  • Dashscope API 密钥

Related MCP server: DeepSeek MCP Server

安装

通过 Smithery 安装

要通过Smithery自动为 Claude Desktop 安装 Qwen Max MCP 服务器：

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

手动安装

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

模型选择

默认情况下，此服务器使用 Qwen-Max 型号。Qwen 系列提供多种具有不同功能的商业型号：

奎恩·马克斯

提供最佳的推理性能，特别是对于复杂和多步骤的任务。

上下文窗口：32,768 个 token

  • 最大输入：30,720 个代币

  • 最大输出：8,192 个代币

  • 定价：$0.0016/1K 代币（输入），$0.0064/1K 代币（输出）

  • 免费额度：100万枚代币

可用版本：

  • qwen-max（稳定版）

  • qwen-max-latest（最新）

  • qwen-max-2025-01-25（快照，也称为 qwen-max-0125 或 Qwen2.5-Max）

Qwen-Plus

性能、速度和成本的平衡组合，非常适合中等复杂的任务。

上下文窗口：131,072 个 token

  • 最大输入：129,024 个 token

  • 最大输出：8,192 个代币

  • 定价：$0.0004/1K 代币（输入），$0.0012/1K 代币（输出）

  • 免费额度：100万枚代币

可用版本：

  • qwen-plus（稳定版）

  • qwen-plus-latest（最新）

  • qwen-plus-2025-01-25（快照，也称为 qwen-plus-0125）

Qwen-Turbo

速度快，成本低，适合简单任务。

  • 上下文窗口：1,000,000 个令牌

  • 最大输入：1,000,000 个代币

  • 最大输出：8,192 个代币

  • 定价：$0.00005/1K 代币（输入），$0.0002/1K 代币（输出）

  • 免费额度：100万枚代币

可用版本：

  • qwen-turbo（稳定版）

  • qwen-turbo-latest（最新）

  • qwen-turbo-2024-11-01（快照，也称为 qwen-turbo-1101）

要修改模型，请在 src/index.ts 中更新模型名称：

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

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

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

有关可用模型的更多详细信息，请访问阿里云模型文档https://www.alibabacloud.com/help/en/model-studio/getting-started/models?spm=a3c0i.23458820.2359477120.1.446c7d3f9LT0FY 。

项目结构

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

配置

  1. 在项目根目录中创建一个.env文件：

DASHSCOPE_API_KEY=your-api-key-here

  1. 更新 Claude Desktop 配置：

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

发展

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

特征

  • 使用 Qwen 模型生成文本

  • 可配置参数（max_tokens、温度）

  • 错误处理

  • MCP 协议支持

  • Claude 桌面集成

  • 支持所有 Qwen 商用型号（Max、Plus、Turbo）

  • 广泛的令牌上下文窗口

API 使用

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

温度参数

温度参数控制模型输出的随机性：

较低值（0.0-0.7）：输出更具针对性和确定性 较高值（0.7-1.0）：输出更具创造性和多样性

根据任务推荐的温度设置：

代码生成：0.0-0.3 技术写作：0.3-0.5 一般任务：0.7（默认） 创意写作：0.8-1.0

错误处理

服务器提供了常见问题的详细错误消息：

API 身份验证错误 无效参数 速率限制 网络问题 超出令牌限制 模型可用性问题

贡献

欢迎贡献代码！欢迎提交 Pull 请求。

执照

麻省理工学院

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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedqwen_max
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.2/5.0

Scored across 1 tool

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

Resources

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