GLM-4.5V MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| read_imageC | 读取本地/URL图片并返回 dataURL 与尺寸信息 |
| vision_queryC | 调用 GLM-4.5V 对图片进行 OCR/问答/检测 |
| process_fileC | 使用 GLM-4.5V 处理文件（上传并提取内容）。支持 PDF、DOCX、DOC、XLS、XLSX、PPT、PPTX、PNG、JPG、JPEG、CSV 等格式 |
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
There is significant overlap between tools, particularly 'process_file' and 'read_image'/'vision_query'. 'process_file' handles image files among others, while 'read_image' and 'vision_query' specifically target images, creating ambiguity about which tool to use for image-related tasks. The descriptions do not clearly delineate boundaries, such as whether 'process_file' extracts text from images or if that's reserved for 'vision_query'.
Tool names follow a consistent snake_case pattern (e.g., 'process_file', 'read_image', 'vision_query'), which is readable and predictable. However, there is a minor deviation in verb style: 'process' and 'read' are action-oriented, while 'vision_query' uses a noun-verb combination, slightly reducing consistency.
With only 3 tools, the server feels thin for a vision/processing domain, potentially limiting functionality. While it covers basic file processing and image tasks, the low count may indicate missing operations for a comprehensive GLM-4.5V integration, such as text analysis or batch processing, making it borderline appropriate.
The tool set has significant gaps for a GLM-4.5V server. It lacks core operations like text querying, model configuration, or error handling tools. There is no clear coverage for non-image file types beyond extraction in 'process_file', and missing update/delete operations for processed data could lead to agent failures in complex workflows.