MCP Servers

protein-lab

Manages local protein experiments with a Flask backend and SQLite, offering tools for protein library management, Beer-Lambert concentration calculations, BLI dilution planning, and experiment archiving. It provides 7 MCP tools for AI assistants like Claude to interact with the system.

README

Protein Lab v0.0.1

本地蛋白质实验管理系统。Flask 后端 + 纯 SQLite，无需网络、数据完全本地。

功能

蛋白库 — 手动添加 / FASTA 批量导入 / 搜索匹配
计算工具 — Beer-Lambert 浓度计算（Pace 1995 消光系数，与 Expasy ProtParam 一致）、BLI 梯度稀释规划（递推稀释 + 统一体积 + 整百微升取整）
实验归档 — 从计算工具一键保存 / 按实验导出 Excel / 历史实验回填
MCP 服务器 — 7 个工具，供 Claude 等 AI 通过 MCP 协议调用

环境要求

Python 3.9+
依赖：pip install -r requirements.txt

启动

双击 启动.bat（Windows）或 启动.command（macOS），浏览器自动打开 http://127.0.0.1:5000。关闭窗口即停止服务。

配置 MCP（可选）

在 Claude Code 的 .claude/settings.json 中添加：

{
  "mcpServers": {
    "protein-lab": {
      "command": "python",
      "args": ["path/to/protein_lab/mcp_server.py"]
    }
  }
}

目录结构

protein_lab/
├── app.py              Flask 主应用
├── calculators.py      计算核心（MW / ε / 浓度 / 稀释）
├── models.py           SQLite 数据模型
├── mcp_server.py       MCP stdio 服务器
├── requirements.txt    依赖
├── 启动.bat            一键启动
├── templates/          Jinja2 页面模板
├── static/             JS + CSS
└── protein_lab.db     自动生成，首次运行创建

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