Vibe Preprocessing and Analysis MCP Server

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:

2. Install the server in Claude Desktop:

Alternative Installation with pip

If you prefer using pip:

Usage

1. Start the MCP server:

2. Test the server using MCP Inspector:

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

Alternatively, you can test it with the MCP Inspector:

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!