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

Add MCP and required dependencies:

uv add "mcp[cli]"
uv add pandas matplotlib seaborn numpy

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

Start the MCP server:

uv run mcp

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!