qwen-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| DASHSCOPE_API_KEY | Yes | Alibaba Cloud DashScope API key. Required for authentication. Alternatively, the key can be provided via a local config.json file, but the environment variable is recommended. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| qwen_visionA | 用通义千问视觉模型 qwen3-vl-plus 理解一张图片（支持本地文件路径或 http(s) 图片 URL），并回答关于图片的问题 |
| qwen_searchA | 用通义千问模型开启联网搜索（enable_search），检索最新信息并返回带来源引用的综合回答；适合时效性问题、新闻、事件、产品信息等 |
| qwen_chatA | 与通义千问文本模型进行普通对话（不联网），用于总结、改写、翻译、代码生成等通用任务 |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: qwen_vision for image understanding, qwen_search for internet-connected queries, and qwen_chat for general text conversations. There is no overlap in functionality, so an agent can easily select the right tool.
All tools follow a consistent qwen_<capability> naming pattern, using lower_snake_case. The suffix clearly indicates the tool's function, making the naming predictable and uniform.
With 3 tools, the server is tightly scoped for its purpose of providing Qwen model access across vision, search, and chat. Each tool serves a distinct core need without unnecessary bloat.
The set covers the primary use cases for a language model server: image understanding, live information retrieval, and general text tasks. However, missing capabilities like audio processing or multi-turn conversation management are minor gaps that can be worked around.