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Vibe Preprocessing and Analysis MCP Server

Vibe Preprocessing and Analysis MCP Server

Public
mudit14224/Vibe-Data-Analysis

Powerful Model Context Protocol server offering advanced CSV data preprocessing, analysis, and visualization tools with customizable workflows and robust error handling for efficient data management.

python
0 tools
May 30, 2025
Updated Jun 4, 2025

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MCP Server

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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 through an intuitive interface.

Features

  • Data Loading and Management

    • Load CSV files from a specified working directory
    • Set and manage working directories
    • List files in the working directory
    • Save processed dataframes to new files
  • Data Preprocessing

    • Handle mixed data types in columns
    • Manage null values with various strategies:
      • Remove rows with nulls
      • Fill with mean/median/mode
      • Forward/backward fill
      • Fill with constant values
    • Drop and rename columns
    • Run custom dataframe editing code
    • Save processed data to new files
  • Data Analysis

    • Generate comprehensive data descriptions
    • Create correlation matrices with visualizations
    • Handle mixed data types in columns
    • Run custom analysis code
  • Data Visualization

    • Create various types of plots:
      • Line plots
      • Bar charts
      • Scatter plots
      • Histograms with KDE
      • Box plots
      • Violin plots
      • Pie charts
      • Count plots
      • Kernel Density Estimation plots
    • Custom graph generation through code
    • Save visualizations to the working directory
    • Run custom visualization code

Setup Instructions

Prerequisites

  • Python 3.x
  • uv (recommended package manager). I recommend using uv to manage the server.

Installation

  1. Add MCP and required dependencies:
uv add "mcp[cli]" uv add pandas matplotlib seaborn numpy
  1. Install the server in Claude Desktop:
mcp install server.py

Alternative Installation with pip

If you prefer using pip:

pip install "mcp[cli]" pandas matplotlib seaborn numpy

Usage

  1. Start the MCP server:
uv run mcp
  1. Test the server using MCP Inspector:
mcp dev server.py

You can install this server in Claude Desktop and interact with it right away by running:

mcp install server.py

Alternatively, you can test it with the MCP Inspector:

mcp dev server.py

Available Tools

Data Management

  • send_work_dir(): Retrieve the current working directory
  • set_work_dir(new_work_dir): Set a new working directory
  • list_work_dir_files(): List files in the current working directory
  • load_csv(filename): Load a CSV file into the system
  • save_global_df(filename): Save the current dataframe to a file

Data Preprocessing

  • handle_column_mixed_types(): Handle columns with mixed data types
  • handle_null_values(strategy, columns): Handle null values in the dataset with various strategies
  • drop_columns(columns): Remove specified columns
  • rename_columns(column_mapping): Rename columns in the dataframe
  • run_custom_df_edit_code(code): Execute custom dataframe manipulation code

Data Analysis

  • describe_df(): Generate a statistical summary of the dataframe
  • generate_correlation_matrix(): Create a correlation matrix with visualization

Data Visualization

  • plot_graph(graph_type, x_column, y_column, output_filename): Create various types of plots
    • Supported graph types: line, bar, scatter, hist, box, violin, pie, count, kde
  • run_custom_graph_code(code): Execute custom visualization code

Environment Variables

  • WORK_DIR: The working directory where files are read from and saved to

Error Handling

The server includes comprehensive error handling for:

  • Missing working directories
  • File not found errors
  • Data loading and processing errors
  • Invalid operations on empty dataframes
  • Mixed data type handling
  • Custom code execution errors
  • Invalid column names
  • Invalid graph types
  • Null value handling errors

Contributing

Feel free to submit issues and enhancement requests!

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