No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by JochenYang · MCP Server · ★ 100
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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Luma MCP 多模型视觉理解 MCP 服务器，为不支持原生视觉能力的 AI 助手提供统一的图片分析能力。 English | 中文 特性 多模型支持：GLM-4.6V、DeepSeek-OCR、Qwen3-VL-Flash、Doubao-Seed-1.6、Hunyuan-Vision-1.5 单工具设计：统一通过 完成图片理解 面向复杂截图优化：支持大图多裁剪、文本密集场景保真处理 统一预处理链路：本地文件、远程 URL、Data URI 都进入同一套处理流程 适用场景完整：代码截图、UI 截图、报错截图、文档截图、OCR 标准 MCP 协议：可接入 Claude Desktop、Cline、Claude Code 等客户端 内置重试：降低临时网络或模型请求失败带来的影响 快速开始 前置要求 Node.js = 18 任意一个模型提供商的 API Key 安装 也可以在 MCP 配置中直接使用： 配置 Claude Desktop 示例 把 和对应密钥替换为你实际使用的提供商： - - - - - - + + （任意 OpenAI 兼容端点） 可选模型覆盖： Custom Provider（v1.5.0+） 使用任意 OpenAI 兼容端点（OpenAI、OpenRouter、Together AI、Anthropic 代理、本地 vLLM/Ollama 等）： bash claude mcp add -s user luma-mcp \ --env MODELPROVIDER=custom \ --env
| Stars | 100 |
| Forks | 11 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.2747241110984/100 |
| Last Updated | 2026-08-09 |
| Created | 2025-11-11 |
| Platforms | mcp, node |
| Est. Tokens | ~15k |
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luma-mcp is 多模型视觉理解 MCP 服务器，为不支持图片理解的 AI 编码模型提供视觉能力：分析截图、报错、UI 与文档，可接入多家主流视觉大模型。Multi-model vision MCP server that adds image understanding to AI coding models without native vision — analyze screenshots, errors,. It is categorized as a MCP Server with 100 GitHub stars.
luma-mcp is primarily written in TypeScript. It covers topics such as image-understanding, mcp, mcp-server.
You can find installation instructions and usage details in the luma-mcp GitHub repository at github.com/JochenYang/luma-mcp. The project has 100 stars and 11 forks, indicating an active community.
luma-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to luma-mcp on Agent Skills Hub include openrouter-mcp-multimodal, airtable-mcp-server, graphlit-mcp-server. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.