glm-vision-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| ZHIPU_API_KEY | Yes | Your Zhipu API key for GLM-4V vision recognition. |
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 |
|---|---|
| describe_imageA | 使用智谱 GLM-4V 识别并描述本地图片内容，或提取图片中的文字。 |
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 one tool, there is no possibility of selecting between overlapping tools. The tool's purpose is clearly described as image description and OCR, so no ambiguity exists.
The single tool name 'describe_image' follows a clear verb_noun pattern, which is internally consistent. There are no other tools to create inconsistencies.
The server offers only one tool, which feels too thin for a vision-focused server. Even though the domain is narrow, typical vision servers provide separate operations for description, OCR, and question answering, making a single catch-all tool insufficient.
The tool covers image description and text extraction, but lacks common vision capabilities such as answering questions about an image or analyzing multiple images. The combined single tool creates a limitation where specific tasks cannot be addressed independently.