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Vibe Preprocessing and Analysis MCP Server for CSV files

@mudit14224

代码仓库

基本信息

分类

数据与分析

传输方式

stdio

作者

mudit14224

关于 Vibe Preprocessing and Analysis MCP Server for CSV files

A powerful MCP (Model Control Protocol) server for preprocessing and analyzing CSV files. This server provides a suite of tools for data manipulation, visualization, and analysis.

#data-analysis#csv-processing#data-visualization

Vibe Preprocessing and Analysis MCP Server for CSV files 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "Vibe-Data-Analysis": {
      "command": "uv",
      "args": [
        "run",
        "mcp"
      ]
    }
  }
}

Vibe Preprocessing and Analysis MCP Server for CSV files 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Vibe Preprocessing and Analysis MCP Server for CSV files?

Vibe Preprocessing and Analysis MCP Server for CSV files is an MCP (Model Control Protocol) server that provides tools for loading, cleaning, analyzing, and visualizing CSV data. It integrates with Claude Desktop and other MCP hosts, and is built on Python with pandas, matplotlib, seaborn, and numpy. It is intended for users who need to preprocess tabular data and generate plots directly through an MCP interface.

How to use Vibe Preprocessing and Analysis MCP Server for CSV files?

Install dependencies with uv add "mcp[cli]" pandas matplotlib seaborn numpy (or pip), then run mcp install server.py to register the server in Claude Desktop. Use mcp dev server.py to test with the MCP Inspector. Set the working directory via the set_work_dir tool or the WORK_DIR environment variable. Use the provided tools in natural language or through the inspector to load, preprocess, analyze, and visualize CSV files.

Key features of Vibe Preprocessing and Analysis MCP Server for CSV files

  • Load CSV files and manage working directories
  • Handle null values with multiple strategies (remove, fill, forward/backward fill)
  • Drop and rename columns, run custom DataFrame editing code
  • Generate statistical summaries and correlation matrices with visualizations
  • Create nine types of plots (line, bar, scatter, histogram, box, violin, pie, count, KDE)
  • Save processed DataFrames and visualizations to the working directory

Use cases of Vibe Preprocessing and Analysis MCP Server for CSV files

  • Clean and prepare messy CSV datasets by handling mixed data types and null values
  • Explore data structure and generate summary statistics and correlation heatmaps
  • Create publication-ready visualizations for exploratory data analysis
  • Automate custom data transformation and plotting logic through code execution
  • Integrate CSV data tasks into an MCP-enabled assistant workflow (e.g., Claude Desktop)

常见问题

1

What are the required dependencies?

Python 3.x, pandas, matplotlib, seaborn, numpy, and the `mcp[cli]` package. The recommended package manager is uv, but pip also works.

2

How do I set the working directory for file operations?

Use the `set_work_dir(new_work_dir)` tool or set the `WORK_DIR` environment variable before starting the server.

3

Which plot types are supported?

The `plot_graph` tool supports: line, bar, scatter, hist (histogram with KDE), box, violin, pie, count, and kde.

4

How can I handle null values in my dataset?

Use the `handle_null_values(strategy, columns)` tool. Supported strategies: remove rows, fill with mean, median, or mode, forward fill, backward fill, or fill with a constant value.

5

Can I run my own code for data manipulation or graphing?

Yes. Use `run_custom_df_edit_code(code)` to modify the DataFrame and `run_custom_graph_code(code)` to generate custom plots. Errors in custom code are caught and reported.

评论

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