cn-llm-bridge
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| QWEN_API_KEY | Yes | 阿里百炼或兼容的API密钥 | |
| BAILIAN_BASE_URL | No | 百炼API基础URL | https://dashscope.aliyuncs.com/compatible-mode/v1 |
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": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| vision_analyzeA | 分析图像内容。支持 URL 或 base64 编码的图像。返回结构化 JSON（含摘要、文字、物体列表）。适合一次性图像分析。 |
| vision_chatA | 多模态对话。支持含图片的多轮对话，自动简洁回复。如果需要一次性结构化分析（JSON 格式输出），请用 vision_analyze。 |
| audio_transcribeA | 本地音频转写。使用 faster-whisper small 模型（CPU-only，INT8 量化）将音频文件转为文字。支持 m4a/mp3/wav/ogg/flac 等常见格式。返回分段文本和全文。 |
| tools_healthA | 检查所有模型 API 的可用状态。返回各模型是否就绪。 |
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 4 tools
vision_analyze and vision_chat both handle images, but they are clearly differentiated by purpose: one for one-time structured analysis, the other for multi-turn conversation. audio_transcribe and tools_health serve distinct functions, leaving minimal ambiguity.
The naming pattern is inconsistent across tools: vision_analyze and vision_chat share a vision_ prefix, audio_transcribe uses audio_, and tools_health uses tools_. While each is readable, there is no uniform verb-noun convention across the set.
With 4 tools, the server is well-scoped for a multimodal bridge covering vision analysis, vision chat, audio transcription, and health checks. The count feels appropriate and not excessive for the apparent purpose.
The tool set covers key multimodal operations: image analysis, image conversation, audio transcription, and system health. Minor gaps exist, such as a text-only chat tool, but the core functionality for a vision/audio bridge is present.