Luma Vision MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| TOP_P | No | Top-P | 0.95 |
| FOCUS_HINT | No | Focus Hint 上下文感知开关 | true |
| LUMA_DEBUG | No | 设为 1 开启调试日志 | |
| MAX_TOKENS | No | 最大输出 token | 8192 |
| MULTI_CROP | No | 大图自动裁剪 | true |
| TEMPERATURE | No | 温度 | 0.7 |
| INCLUDE_META | No | 结果末尾附加调用元信息 | false |
| ZHIPU_API_KEY | No | 智谱 API Key | |
| CUSTOM_API_KEY | No | 自定义端点 API Key | |
| FALLBACK_CHAIN | No | 备用 Provider 链，逗号分隔 | |
| MODEL_PROVIDER | No | 主 Provider | zhipu |
| CUSTOM_BASE_URL | No | 自定义端点 Base URL | |
| ENABLE_THINKING | No | 视觉模型深度思考 | true |
| HUNYUAN_API_KEY | No | 腾讯混元 API Key | |
| CUSTOM_MODEL_NAME | No | 自定义模型名称 | |
| DASHSCOPE_API_KEY | No | 阿里云 API Key | |
| FALLBACK_STRATEGY | No | sequential 或 race | sequential |
| VOLCENGINE_API_KEY | No | 火山引擎 API Key | |
| SILICONFLOW_API_KEY | No | 硅基流动 API Key | |
| MULTI_CROP_MAX_TILES | No | 最大裁剪数 | 5 |
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 |
|---|---|
| image_understandA | 通用图像理解工具。当用户提到看图/截图/界面/报错/布局，或对话中出现图片附件并询问图片相关问题时调用。 图片来源：粘贴图路径、本地路径、HTTP(S) URL、Data URI。 直接传入用户原始问题即可，服务端会自动注入 Focus Hint 和基础视觉协议。 task_type（可选）：auto|general|ocr|ui|debug|describe。省略或auto时自动推断。 |
| image_ocrA | 专用OCR文字提取工具。从图片中高精度提取所有文字内容，保留格式和阅读顺序。 适合场景：文档截图、代码截图、表格、表单、扫描件。 如果是PDF文档，建议使用本地MinerU skill处理。 |
| image_compareA | 对比两张图片的差异。适合场景：设计稿vs实现、修改前后对比、找不同。 返回结构化的差异报告，包含差异类型、严重级别和描述。 |
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 3 tools
Each tool has a clearly distinct purpose: general understanding, high-precision OCR, and image comparison. The optional OCR task type in image_understand could cause minor overlap, but the descriptions clearly position image_ocr as the specialized tool.
All tool names share the 'image_' prefix and use snake_case, which is consistent. However, 'image_ocr' uses an acronym while the others use verbs, creating a minor stylistic inconsistency.
With only 3 tools, the server is well-scoped and each tool covers a fundamental vision task. This is appropriate for a focused utility without unnecessary bloat.
The toolset covers the core image understanding workflows: general QA, text extraction, and diff comparison. Minor gaps like explicit image metadata extraction or more granular analysis options exist, but the primary use cases are well supported.