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Qwen Max MCP Server

by kamelirzouni
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JavaScript
Hybrid

Qwen Max MCP 서버

Qwen Max 언어 모델을 위한 MCP(모델 컨텍스트 프로토콜) 서버 구현.

대장간 배지

Node.js를 사용하는 이유는 무엇일까요? 이 구현은 Node.js/TypeScript를 사용합니다. Python과 같은 다른 언어에 비해 MCP 서버와의 통합이 가장 안정적이고 신뢰할 수 있기 때문입니다. MCP용 Node.js SDK는 더 나은 타입 안전성, 오류 처리 및 Claude Desktop과의 호환성을 제공합니다.

필수 조건

  • Node.js(v18 이상)

  • 엔피엠

  • 클로드 데스크탑

  • Dashscope API 키

Related MCP server: DeepSeek MCP Server

설치

Smithery를 통해 설치

Smithery를 통해 Claude Desktop에 Qwen Max MCP Server를 자동으로 설치하려면:

지엑스피1

수동 설치

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

모델 선택

기본적으로 이 서버는 Qwen-Max 모델을 사용합니다. Qwen 시리즈는 다양한 기능을 갖춘 여러 상용 모델을 제공합니다.

퀀-맥스

특히 복잡하고 여러 단계로 구성된 작업에 대해 최고의 추론 성능을 제공합니다.

컨텍스트 창: 32,768개 토큰

  • 최대 입력: 30,720 토큰

  • 최대 출력: 8,192개 토큰

  • 가격: $0.0016/1K 토큰(입력), $0.0064/1K 토큰(출력)

  • 무료 할당량: 100만 토큰

사용 가능한 버전:

  • qwen-max(안정적)

  • qwen-max-latest (최신)

  • qwen-max-2025-01-25(스냅샷, qwen-max-0125 또는 Qwen2.5-Max라고도 함)

퀀플러스

성능, 속도, 비용의 균형 잡힌 조합으로, 적당히 복잡한 작업에 이상적입니다.

컨텍스트 창: 131,072개 토큰

  • 최대 입력: 129,024개 토큰

  • 최대 출력: 8,192개 토큰

  • 가격: $0.0004/1K 토큰(입력), $0.0012/1K 토큰(출력)

  • 무료 할당량: 100만 토큰

사용 가능한 버전:

  • qwen-plus(안정적)

  • qwen-plus-latest (최신)

  • qwen-plus-2025-01-25 (스냅샷, qwen-plus-0125라고도 함)

퀀-터보

속도가 빠르고 비용이 저렴하여 간단한 작업에 적합합니다.

  • 컨텍스트 창: 1,000,000개 토큰

  • 최대 입력: 1,000,000 토큰

  • 최대 출력: 8,192개 토큰

  • 가격: $0.00005/1K 토큰(입력), $0.0002/1K 토큰(출력)

  • 무료 할당량: 100만 토큰

사용 가능한 버전:

  • qwen-turbo(안정적)

  • qwen-turbo-latest (최신)

  • qwen-turbo-2024-11-01 (스냅샷, qwen-turbo-1101로도 알려짐)

모델을 수정하려면 src/index.ts에서 모델 이름을 업데이트하세요.

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

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

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

사용 가능한 모델에 대한 자세한 내용은 Alibaba Cloud 모델 설명서 https://www.alibabacloud.com/help/en/model-studio/getting-started/models?spm=a3c0i.23458820.2359477120.1.446c7d3f9LT0FY에서 확인하세요.

프로젝트 구조

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

구성

  1. 프로젝트 루트에 .env 파일을 만듭니다.

DASHSCOPE_API_KEY=your-api-key-here

  1. Claude Desktop 구성 업데이트:

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

개발

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

특징

  • Qwen 모델을 사용한 텍스트 생성

  • 구성 가능한 매개변수(max_tokens, 온도)

  • 오류 처리

  • MCP 프로토콜 지원

  • Claude Desktop 통합

  • 모든 Qwen 상용 모델(Max, Plus, Turbo) 지원

  • 광범위한 토큰 컨텍스트 창

API 사용

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

온도 매개변수

온도 매개변수는 모델 출력의 무작위성을 제어합니다.

낮은 값(0.0-0.7): 보다 집중적이고 결정론적인 결과물 높은 값(0.7-1.0): 보다 창의적이고 다양한 결과물

작업별 권장 온도 설정:

코드 생성: 0.0-0.3 기술 문서 작성: 0.3-0.5 일반 작업: 0.7(기본값) 창의적 문서 작성: 0.8-1.0

오류 처리

서버는 일반적인 문제에 대한 자세한 오류 메시지를 제공합니다.

API 인증 오류 잘못된 매개변수 속도 제한 네트워크 문제 토큰 제한 초과 모델 가용성 문제

기여하다

기여를 환영합니다! 풀 리퀘스트를 제출해 주세요.

특허

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.

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

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