Pandas MCP Server

This repository contains a server implementation using the Model Context Protocol (MCP) with functionalities to handle CSV files, execute Pandas code, and generate interactive charts (bar charts and pie charts).

Requirements

• Python 3.11 or higher
• Install required packages: pip install -r requirements.txt

Functions

read_metadata

• Description: Loads a CSV file and returns its column structure and sample data.
• Parameters:
  • file_path: Path to the CSV file.
• Returns:
  • A dictionary containing the columns and sample data from the CSV file.
• Notes:
  • Detects file encoding and delimiter automatically.
  • Limits file size to 100MB to prevent excessive memory usage.

run_pandas_code

• Description: Executes Pandas code provided as a string.
• Parameters:
  • code: String containing the Pandas code to execute.
• Returns:
  • A dictionary containing the result of the executed code and any variables created during execution.
• Security Notes:
  • Prevents execution of blacklisted operations such as os., sys., subprocess., open(, exec(, eval(, import os, import sys.
  • Provides detailed error messages and suggestions to help users resolve issues.

bar_chart_to_html

• Description: Generates an interactive HTML bar chart using Chart.js template.
• Parameters:
  • categories: List of category names for x-axis
  • values: List of numeric values for y-axis
  • title: Chart title (default: "Interactive Chart")
• Returns:
  • A dictionary containing the file path and status information
• Example:

{
    "categories": ["Electronics", "Clothing", "Home Goods"],
    "values": [120000, 85000, 95000],
    "title": "Q1 Sales by Product Category"
}

pie_chart_to_html

• Description: Generates an interactive HTML pie chart using Chart.js template.
• Parameters:
  • labels: List of label names for each pie slice
  • values: List of numeric values for each slice
  • title: Chart title (default: "Interactive Pie Chart")
• Returns:
  • A dictionary containing the file path and status information
• Example:

{
    "labels": ["Electronics", "Clothing", "Home Goods"],
    "values": [120000, 85000, 95000],
    "title": "Q1 Sales Distribution"
}

line_chart_to_html

• Description: Generates an interactive HTML line chart using Chart.js template.
• Parameters:
  • labels: List of label names for x-axis
    • datasets: List of datasets, each containing:
      • label: Name of the dataset
      • data: List of numeric values
  • title: Chart title (default: "Interactive Line Chart")
• Returns:
  • A dictionary containing the file path and status information
• Example:

{
    "labels": ["Jan", "Feb", "Mar"],
    "datasets": [
        {
            "label": "Sales",
            "data": [12000, 15000, 18000]
        },
        {
            "label": "Expenses",
            "data": [8000, 9000, 10000]
        }
    ],
    "title": "Monthly Performance"
}

Usage

1. Configure your MCP client with the following settings:

{
  "mcpServers": {
    "pandas": {
      "name": "pandas",
      "type": "stdio",
      "description": "run pandas code",
      "isActive": true,
      "command": "python",
      "args": [
        "${workspaceFolder}/server.py"
      ]
    }
  }
}

2. Use the configured MCP client to interact with the server and utilize the provided tools.

Workflow

1. Read metadata of your CSV file:

   • User prompt: "Read metadata of data/sample.csv and show me the column structure"
   • This will call read_metadata with the file path

2. Execute Pandas operations on the loaded data:

   • User prompt: "Group the data by category and calculate the sum for each group"
   • This will call run_pandas_code with the appropriate Pandas operation

3. Visualize the results using charts:

   • User prompt: "Create a bar chart showing sales by category"
   • This will call bar_chart_to_html with the grouped data
   • Example response:

{
    "status": "SUCCESS",
    "filepath": "/absolute/path/to/chart_1713443200.html"
}