vision-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| VISION_MODEL | Yes | 模型名 | |
| VISION_API_KEY | Yes | API密钥 | |
| VISION_BASE_URL | Yes | API基础地址 |
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 |
|---|---|
| analyze_imageB | Analyze an image and return a detailed description. Supports local file paths and http(s) URLs. |
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
Only one tool exists, so there is no possibility of confusion between tools.
With a single tool, naming is inherently consistent. 'analyze_image' follows a clear verb_noun pattern.
A single tool for a vision MCP feels thin; typical vision services offer multiple capabilities (e.g., object detection, OCR). It is borderline acceptable for a very focused use case.
Only one generic image analysis tool is provided. Missing common operations like listing supported formats, specific detection tasks, or batch processing, which creates significant gaps for most use cases.