protein-lab
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., "@protein-labPlan a BLI dilution series for my 2 mg/mL stock"
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.
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
Related MCP server: ChEMBL MCP Server
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This server cannot be installed
Maintenance
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