lanhu-mcp-codex
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@lanhu-mcp-codexgenerate design context from Lanhu link https://lanhuapp.com/pid/123"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
lanhu-mcp-codex
蓝湖只读 MCP 服务,用于把蓝湖项目/设计稿链接转换成 Codex 或其他 AI Agent 可消费的设计上下文。
当前能力
解析蓝湖链接中的
pid、tid、image_id、docType等参数。通过蓝湖 Web API 只读获取项目画板列表。
下载可访问的画板缩略图。
生成本地
context.md和context.json。提供 stdio MCP 工具,适合 Codex 本地接入。
Related MCP server: pymupdf4llm-mcp
快速开始
npm install
npm run build
npm test设置蓝湖 Cookie:
LANHU_COOKIE="从蓝湖请求中复制的 Cookie 请求头"启动 MCP:
node dist/index.jsCodex MCP 配置示例
将路径替换为本机仓库绝对路径:
[mcp_servers.lanhu-readonly]
type = "stdio"
command = "node"
args = ["C:\\path\\to\\lanhu-mcp-codex\\dist\\index.js"]
[mcp_servers.lanhu-readonly.env]
LANHU_COOKIE = "your_lanhu_cookie"MCP 工具
lanhu_parse_url:解析蓝湖链接。lanhu_list_project_images:读取项目画板列表,不落盘。lanhu_get_design_context:生成本地上下文与缩略图资源。
默认产物目录:
.lanhu-mcp.local/runs/{timestamp}-{pidShort}/
├── context.json
├── context.md
└── images/多 Agent 协作入口
新用户或新 AI Agent 接手前,按顺序阅读:
README.mdAGENTS.mddocs/PROJECT.mddocs/STATUS.mddocs/ROADMAP.mddocs/HANDOFF.mddocs/DECISIONS.mddocs/CODE_STANDARDS.md
每次阶段性开发结束前,需要:
跑
npm run typecheck、npm test、npm run build。更新
docs/STATUS.md和docs/HANDOFF.md。如果变更影响路线或架构,更新
docs/ROADMAP.md或docs/DECISIONS.md。使用中文 Conventional Commits 提交,例如
feat: 增强蓝湖画板规范化逻辑。
已有本地仓库恢复工作时:
git status --short --branch
git pull --ff-only
npm install
npm run typecheck
npm test
npm run build拉取后重新阅读 AGENTS.md、docs/STATUS.md、docs/HANDOFF.md、docs/ROADMAP.md、docs/DECISIONS.md,再继续开发。
安全边界
不提交真实 Cookie、Token、账号信息。
不打印、不保存、不写入
LANHU_COOKIE。.lanhu-mcp.local/、dist/、node_modules/、work/、outputs/不提交。V1 不做任何蓝湖写操作。
Maintenance
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