image-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| ZHIPU_MODEL | No | Zhipu model name (e.g., glm-4v). Optional, defaults to code default. | |
| OLLAMA_MODEL | No | Ollama model name (e.g., llava). Optional, defaults to code default. | |
| ZHIPU_API_KEY | No | Zhipu API key (format: xxx.xxx). Required if using Zhipu provider. | |
| ANTHROPIC_MODEL | No | Anthropic model name (e.g., claude-sonnet-4-5-20250929). Optional, defaults to code default. | |
| OLLAMA_BASE_URL | No | Ollama base URL (e.g., http://localhost:11434). Optional, defaults to http://localhost:11434. | |
| DEFAULT_PROVIDER | No | Default provider to use (anthropic, zhipu, or ollama). Optional, auto-selects first configured backend. | |
| ANTHROPIC_API_KEY | No | Claude (Anthropic) API key. Required if using Anthropic provider. |
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 |
|---|---|
| vision_describeB | 对一张本地图片生成详细的文字描述/识别结果 |
| vision_qaA | 针对一张本地图片提问，模型根据图片内容回答 |
| vision_analyzeB | 对多张本地图片进行综合分析/对比，根据自定义 prompt 给出结论 |
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
Each tool targets a distinct task: multi-image analysis with custom prompt, single-image description, and single-image Q&A. No overlap in functionality.
All tools follow consistent 'vision_<verb>' pattern with clear, descriptive verbs (analyze, describe, qa).
Three tools is well-scoped for an image analysis server, covering the essential operations without bloat or insufficiency.
The set covers core image analysis tasks (description, QA, multi-image comparison). Missing potential features like image search or generation, but these are beyond the stated domain.