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mcp-data-analysis

Analyze CSV datasets end-to-end with MCP tools for EDA, statistics, and publication-ready outputs.

2|Updated Jan 26, 2026
Search Tags:#mcp#data-analysis#eda#data-preparation#missing-values#tableone#correlations
One-click install
npx skills add https://github.com/u9401066/automl-stat-mcp --skill mcp-data-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mcp-data-analysis
Source: https://github.com/u9401066/automl-stat-mcp/tree/main/.claude/skills/mcp-data-analysis
Command: npx skills add https://github.com/u9401066/automl-stat-mcp --skill mcp-data-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This workflow enables researchers and analysts to perform a complete data analysis pipeline using MCP tools on CSV datasets, eliminating manual tool switching and ensuring consistent outputs.

Core Features & Use Cases

  • End-to-end data analysis workflow including EDA, Table One, descriptive statistics, and correlation analysis.
  • Automatic path normalization to container-friendly /data paths and guided result storage.
  • Quick-start templates for exploratory analysis, missing-value checks, and auto-analysis to generate publication-ready outputs.

Quick Start

Provide a CSV path to start a full MCP data analysis workflow, and the agent will preview the data, summarize columns, and generate a Table One report.

Frequently Asked Questions about mcp-data-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run end-to-end exploratory data analysis on a CSV file?▼

To perform EDA on a CSV file, provide the dataset path to trigger an automated workflow that previews data, summarizes columns, checks missing values, and generates a Table One report.

What is the best way to generate a Table One report for publication from raw data?▼

Generating a publication-ready Table One report is done by applying an automated data analysis workflow that calculates descriptive statistics and group comparisons directly from your input CSV dataset.

How does MCP handle path normalization for data analysis workflows?▼

Path normalization for MCP data analysis works by automatically converting input dataset locations to container-friendly /data paths, ensuring secure, structured result storage across Redis and MinIO environments.

Can I automatically check missing values and correlations in my dataset?▼

Yes, you can automatically check missing values and compute correlations by applying a guided data preparation workflow that evaluates columns and outputs structured summary statistics for your CSV data.

Does this data analysis workflow support interactive tool selection during execution?▼

Yes, the data analysis workflow supports interactive tool selection during execution, allowing you to choose specific analytical steps like EDA or correlation analysis while maintaining secure structured outputs.

Why do I need to normalize dataset paths to /data paths before analysis?▼

Normalizing dataset paths to /data paths is required to satisfy workflow constraints for container environments, enabling automatic path handling and secure structured storage in Redis or MinIO.

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