glm4v-vision-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| ZHIPU_API_KEY | Yes | 智谱 AI API Key (required) |
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 | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| analyze_imageC | 使用 GLM-4V Flash 分析图片内容，支持中文和英文描述 |
| extract_textB | 从图片中提取文字（OCR 功能） |
| describe_imageA | 生成图片的详细描述，用于图像标注或 Accessibility |
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 3 tools
describe_image and analyze_image have overlapping purposes—both generate descriptions of image content, making it unclear which to use. extract_text is distinct, but the boundary between the other two is ambiguous.
All tool names follow a consistent verb_noun pattern (describe_image, analyze_image, extract_text) using snake_case. No stylistic deviations.
With 3 tools, the server is well-scoped for a vision-focused MCP. Each tool addresses a core image understanding task without unnecessary bloat.
The toolset covers basic image description, analysis, and OCR, which are the primary vision tasks. Minor gaps exist (e.g., no image comparison or specific object detection), but the core surface is complete.