使用LLM生成代码来对Excel进行分析的MCP Server
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"chatbi-mcp-server": {
"command": "<see-readme>",
"args": []
}
}
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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。
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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。
uv venv .venv --python=3.11
source .venv/bin/activate
uv pip install -r requirements.txt
cd src
python pandas_mcp_server.py
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。

使用时,记得开启这个MCP Server:


结合mcp-server-chart使用

通过指令,自动化数据分析:
生成数据分析计划:
开始分析:
结果报告:

生成的看板:
