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!