CSV Analysis

Provides advanced CSV analysis, visualizations, and thinking outputs powered by Gemini AI.

javascript

GitHub Stars

javascript

Language

5 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "falahgs-mcp-csv-analysis-with-gemini-ai": {
      "command": "node",
      "args": [
        "path/to/mcp-csv-analysis-gemini/dist/index.js"
      ],
      "env": {
        "GEMINI_API_KEY": "YOUR_GEMINI_API_KEY",
        "PLOTLY_API_KEY": "YOUR_PLOTLY_API_KEY",
        "PLOTLY_USERNAME": "YOUR_PLOTLY_USERNAME"
      }
    }
  }
}

You have a dedicated MCP Server for CSV analysis and Gemini-powered thinking. This server analyzes CSV data, generates insightful visualizations, and provides detailed thinking outputs, helping you extract business-relevant insights from your data. It runs locally and integrates with your MCP client to streamline data workflows.

How to use

You interact with the server through your MCP client to perform three main tasks: analyze CSV data, visualize data, and generate thinking outputs. Start by pointing the server at your CSV file and choosing the analysis depth. Then request interactive visualizations for selected columns, and finally generate a structured thinking trace of your analysis.

Practical usage patterns you can follow:

How to install

Prerequisites you need before installing and running the MCP server are listed here so you can prepare your environment.

# Install Node.js (v16+)
# Use your preferred method to install Node.js

# Install dependencies for the MCP server
npm install

# Build the project before running
npm run build

Additional sections

Configuration and security details are important to ensure smooth operation and protect sensitive data.

Environment variables needed to run the server include your Gemini API key and Plotly credentials. Keep these secure and do not commit them to version control.

You can customize where outputs are saved by providing an explicit output directory in your requests to separate analyses, visualizations, and thinking traces.

If you encounter issues, review your configuration, verify file permissions for the CSV data, and ensure your API keys are valid.

Available tools

analyze-csv

Performs Exploratory Data Analysis on a CSV file with basic or detailed analysis modes, including data quality, correlations, feature importance, and business insights.

visualize-data

Generates interactive visualizations using Plotly. Supports basic, advanced, or custom chart configurations with multiple chart types.

generate-thinking

Produces a structured thinking trace describing the analysis process, with timestamps and optional output directory.

Built by

VeilStrat

AI signals for GTM teams