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ChatBI MCP Server

by Steven-Luo
GitHub
Python
10
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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

This server cannot be installed

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security - not tested
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license - not tested
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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.

  1. 准备环境
    1. 配置LLM API Key
    2. 安装依赖
  2. 运行服务
    1. 使用
      1. 配置ChatBI MCP Server
      2. 使用ChatBI MCP Server
      3. 综合运用

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