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：

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Enables AI-powered business intelligence and data analysis using pandas and LLM code generation. Supports automated data processing, statistical analysis, and visualization creation through natural language interactions.

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ChatBI MCP Server