What is Vibe Data Analysis

Vibe-Data-Analysis is a powerful MCP (Model Control Protocol) server designed for preprocessing and analyzing CSV files. It offers a comprehensive suite of tools for data manipulation, visualization, and analysis through an intuitive interface.

Use cases

Use cases include cleaning and preparing datasets for analysis, generating visual reports for presentations, performing exploratory data analysis, and creating custom visualizations for specific data insights.

How to use

To use Vibe-Data-Analysis, start the MCP server with the command ‘uv run mcp’. You can then load CSV files, preprocess data, perform analysis, and create visualizations using the provided tools and commands.

Key features

Key features include data loading and management, data preprocessing (handling null values, renaming columns), data analysis (generating descriptions and correlation matrices), and data visualization (creating various plots such as line plots, bar charts, and histograms).

Where to use

Vibe-Data-Analysis can be used in various fields including data science, business analytics, research, and any domain that requires data manipulation and analysis of CSV files.

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

2. 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

2. 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!