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luma-mcp

Multi-Model Visual Understanding MCP Server, GLM-4.6V, DeepSeek-OCR (free), and Qwen3-VL-Flash. Provide visual processing capabilities for AI coding models that do not support image understanding.多模型视觉理解MCP服务器，GLM-4.6V、DeepSeek-OCR（免费）和Qwen3-VL-Flash等。为不支持图片理解的 AI 编码模型提供视觉处理能力。

→ JochenYang/luma-mcp· listed on github_topic
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// key findings
⚠️
Known vulnerabilities in dependencies: 9 high
Affects packages this MCP installs at runtime. Upgrade or remove the affected dependency.
✅
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
✅
Open source with a license and README
Anyone can audit the code, the license is declared, and the publisher documents what it does.
🔐
You'll be asked for 6 credentials: CUSTOM_API_KEY, DASHSCOPE_API_KEY, HUNYUAN_API_KEY, SILICONFLOW_API_KEY, VOLCENGINE_API_KEY, ZHIPU_API_KEY
These are read from process.env at runtime. Make sure you trust where they’ll be sent.
// known CVEs in dependencies9 high2 low

Disclosed vulnerabilities in this server's declared npm dependencies (via OSV). Whether each is reachable depends on the installed versions.

high@modelcontextprotocol/sdk@1.25.4GHSA-345p-7cg4-v4c7

@modelcontextprotocol/sdk has cross-client data leak via shared server/transport instance reuse

highaxios@1.15.1GHSA-35jp-ww65-95wh

axios Vulnerable to Full Man-in-the-Middle via Prototype Pollution Gadget in `config.proxy`

highaxios@1.15.1GHSA-3g43-6gmg-66jw

axios Vulnerable to Credential Theft and Response Hijacking via Prototype Pollution Gadget in Config Merge

highaxios@1.15.1GHSA-777c-7fjr-54vf

Allocation of Resources Without Limits or Throttling in Axios

highaxios@1.15.1GHSA-hfxv-24rg-xrqf

Axios: Regular Expression Denial of Service (ReDoS) via Cookie Name Injection

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// required environment variables
This server reads these from process.env. You'll be asked to provide them before it can run.
configBASE_VISION_PROMPT— 内置默认值 自定义基础视觉提示词
🔐 secretCUSTOM_API_KEY— custom -> + CUSTOM_BASE_URL + CUSTOM_MODEL_NAME（任意 OpenAI 兼容端点）
configCUSTOM_AUTH_HEADER— bearer — bearer / x-api-key / custom
configCUSTOM_AUTH_HEADER_VALUE— "X-API-Key: {{key}}" — 自定义 Header 模板
configCUSTOM_BASE_URL— custom -> CUSTOM_API_KEY + + CUSTOM_MODEL_NAME（任意 OpenAI 兼容端点）
configCUSTOM_MODEL_NAME— custom -> CUSTOM_API_KEY + CUSTOM_BASE_URL + （任意 OpenAI 兼容端点）
configCUSTOM_PATH— /chat/completions — API 路径
configCUSTOM_THINKING_MODE— disabled — disabled / openai / qwen_extra_body
configCUSTOM_TIMEOUT_MS— 60000 — 超时毫秒
🔐 secretDASHSCOPE_API_KEY— claude mcp add -s user luma-mcp --env MODEL_PROVIDER=qwen --env =your-api-key -- npx -y luma-mcp
configENABLE_THINKING
🔐 secretHUNYUAN_API_KEY— hunyuan ->
configMAX_TOKENS— 8192 最大生成 token 数（部分模型有硬上限，详见下方说明）
configMODEL_NAME— doubao-seed-1-6-flash-250828
configMODEL_PROVIDER— "": "zhipu",
configMULTI_CROP
configMULTI_CROP_MAX_TILES
🔐 secretSILICONFLOW_API_KEY— siliconflow ->
configTOP_P
🔐 secretVOLCENGINE_API_KEY— volcengine ->
🔐 secretZHIPU_API_KEY— "": "your-api-key"
// full audit trail
The full breakdown of what we checked, the deductions that landed, the network hosts, the dependency advisories, and concrete fix guidance is available to verified publishers.
// improvement guidance — verified publishers only
We have 5 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
// embed badge in your README
[![M8ven Score](https://m8ven.ai/badge/mcp/jochenyang-luma-mcp-guaqp3)](https://m8ven.ai/mcp/jochenyang-luma-mcp-guaqp3)
commit: 9831a4861960cabeb1f10d834b7e081aa557eda5
code hash: 376a826dc6b940eaaf33178511693d959725f8adb15aaa29fbcb5c8fe2b246db
verified: 6/15/2026, 1:35:57 PM
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