Image Processor 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 |
|---|---|
| process_image_to_codeC | 处理图像并生成代码 |
| process_image_to_descriptionC | 处理图像并生成描述 |
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 2 tools
The two tools have clearly distinct purposes: one generates code from images, and the other generates descriptions from images. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the desired output type.
Both tools follow a consistent verb_noun pattern with 'process_image_to_' as a prefix, followed by the specific output type ('code' or 'description'). This naming scheme is predictable and enhances readability across the tool set.
With only two tools, the server feels thin for an 'Image Processor' domain, which might imply broader capabilities like image editing, filtering, or analysis. The limited scope suggests potential gaps in functionality that could hinder agent workflows.
The tool set is severely incomplete for an image processing domain. It lacks basic operations such as image resizing, format conversion, filtering, or analysis tools, focusing only on generation tasks. This creates significant gaps that will likely cause agent failures when broader image processing needs arise.