Vibe Preprocessing and Analysis MCP Server for CSV files

Name: Vibe Preprocessing and Analysis MCP Server for CSV files
Author: mudit14224

@mudit14224

Vibe Preprocessing and Analysis MCP Server for CSV files について

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

基本情報

カテゴリ

データと分析

ランタイム

python

トランスポート

stdio

公開者

mudit14224

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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)

FAQ from Vibe Preprocessing and Analysis MCP Server for CSV files

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.

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.

Which plot types are supported?

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

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.

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.