Vision-Multi MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| VISION_MODEL | No | 默认后端模型 | qwen3.7-flash |
| VISION_PROVIDERS | No | 附加后端 JSON 数组 | |
| DASHSCOPE_API_KEY | No | 默认后端 API key（至少需要一个后端） | |
| VISION_STATE_FILE | No | 状态文件路径 | <REASONIX_HOME>/mcp-state/vision-multi-state.json |
| DASHSCOPE_BASE_URL | No | 默认后端接口地址 | https://dashscope.aliyuncs.com/compatible-mode/v1 |
| VISION_FAIL_THRESHOLD | No | 连续失败多少次后自动切换默认后端 | 1 |
| VISION_DEFAULT_PROVIDER | No | 默认后端 id（不传时按 apiKey 已配置的第一个后端） |
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 |
|---|---|
| analyze_imageA | 使用视觉大模型分析图片内容。传入本地图片文件路径或 http(s) 图片 URL，可附带问题。可用 provider 参数手动切换识别后端（用 list_providers 查看），可用 model 参数覆盖该后端的默认模型。 |
| list_providersA | 列出当前配置的所有视觉识别后端（id / 名称 / 模型 / 接口地址），不含密钥。用于手动切换前查看可选后端。 |
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 2 tools
The two tools have clearly distinct purposes: list_providers shows available backend configurations, while analyze_image performs image analysis. There is no overlap or ambiguity between them.
Both tool names follow the consistent verb_noun pattern: list_providers and analyze_image. This is a predictable and uniform naming convention.
With only 2 tools, the set is minimal but well-suited to the server's focused purpose of image analysis with configurable providers. Each tool serves a distinct and necessary function, so the count feels appropriate rather than thin.
The core workflow is covered: list available providers and analyze an image, with the ability to select a provider or model. A minor gap is the lack of provider management operations, but these are typically handled by configuration files rather than MCP tools.