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ChatBI MCP Server

准备环境

配置LLM API Key

cp .env.example .env

编辑.env文件,填写LLM信息,要生成代码,建议填写相对比较强的模型,比如GLM 4.5、Qwen-235B-A22B、Kimi K2等,至少Qwen3-32B,规模再小的生成代码质量会比较差,分析效果差。

安装依赖

uv venv .venv --python=3.11 source .venv/bin/activate uv pip install -r requirements.txt

运行服务

cd src python pandas_mcp_server.py

使用

配置ChatBI MCP Server

使用任意支持MCP Server的客户端,比如Cherry Studio,配置如下:

其中验证信息,在config.yaml中,可以自行修改。默认值为eyJzdWIiOiAidXNlcjEyMyIsICJpYXQiOiAxNzUxODA5ODIwLCAiZXhwIjogMTc1MTgxMzQyMH0

注意:超时时间设置长一点,因为涉及LLM生成代码、如果出错还需要改错

添加完成后,点击“保存”,然后点击又上方的开启选项,切换到“工具”标签页:

如果能正常列出工具,说明配置正确。

使用ChatBI MCP Server

常规统计

使用时,记得开启这个MCP Server:

使用1.png

使用1-2.png

可视化

结合mcp-server-chart使用

使用2.png

综合运用

通过指令,自动化数据分析:

生成数据分析计划: 综合1.png 开始分析: 综合2.png 结果报告: 综合3.png 综合4.png

生成的看板: Dashboard.png

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