vision-mcp-ms
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| API_KEY | No | Generic API key | |
| VISION_MODELS | No | Comma-separated ordered list of vision models | Qwen/Qwen2.5-VL-7B-Instruct |
| OPENAI_API_KEY | No | API Key for OpenAI-compatible service (also compatible with SILICONFLOW_API_KEY / API_KEY) | |
| OPENAI_BASE_URL | No | OpenAI-compatible API base URL | https://api.siliconflow.cn/v1 |
| SILICONFLOW_API_KEY | No | Alternative API key for SiliconFlow | |
| VISION_REQUEST_TIMEOUT_MS | No | Timeout for each model request in milliseconds | 60000 |
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
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| analyze_imageA | 分析一张图片，返回视觉模型给出的文字结果。 image 可以是：HTTP/HTTPS 图片 URL、base64 data URL（data:image/...）。 按 VISION_MODELS 配置的顺序调用模型，遇限流/超时/5xx 自动切换到下一个。 |
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 1 tool
With only a single tool, there is no possibility of confusion between tools. Analyze_image is the sole operation, so selection is trivially unambiguous.
The tool name follows a clear verb_noun pattern (analyze_image). With only one tool, consistency is inherent and the name accurately describes the operation.
The server is named vision-mcp-ms, implying a broader vision scope, yet it exposes only one tool. This feels too few for the apparent domain, as typical vision MCPs offer multiple operations (e.g., OCR, object detection, image generation).
The analyze_image tool covers the core need of image analysis and returns text, but there are minor gaps such as no support for batch processing, no explicit model selection, or output format options. These are workarounds but leave the surface slightly incomplete.