eda-mcp 0.1.0

MCP server for exploratory data analysis

eda-mcp

An MCP server for exploratory data analysis. Point it at a dataset and let your AI assistant do the analysis — summary statistics, diagnostic plots, correlation analysis, and full markdown reports, all from a single conversation.

Built by MLMecham.

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Quickstart

Run instantly with no install step:

uvx eda-mcp

Or install permanently:

pip install eda-mcp

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Connecting to Claude Desktop

Add this to your claude_desktop_config.json:

Mac: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "eda-mcp": {
      "command": "uvx",
      "args": ["eda-mcp"]
    }
  }
}

Restart Claude Desktop. The tools will appear automatically.

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Tools

Tool	Description
load_dataset	Load a file and get column names, types, classifications, and missing value counts. Start here.
get_column_summary	Full statistics for a single column — five-number summary, skewness, kurtosis, outlier count, normality test.
get_all_summaries	Summary statistics for every column at once, keyed by column name.
get_diagnostic_plot	Generate a diagnostic plot for a single column. Plot type is auto-selected by classification.
get_correlations	Pearson and Spearman correlation matrices, a heatmap, and scatter plots for strongly correlated pairs.
generate_report	Full EDA report — dataset overview, data quality flags, per-column summaries with plots, and correlation analysis. Saved as markdown.

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Supported File Formats

Format	Extension
CSV	.csv
Parquet	.parquet
Excel	.xlsx, .xls
JSON	.json
Newline-delimited JSON	.ndjson
Avro	.avro
SQLite	.db, .sqlite

String columns are automatically coerced to better types on load (integers, floats, dates) where unambiguous.

For SQLite files with multiple tables, pass the table parameter to specify which one.

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Column Classifications

Every column is automatically classified before analysis:

Classification	Description
continuous	Floats, or integers with more than 20 unique values
discrete	Integers with 20 or fewer unique values
categorical	Strings with low cardinality (< 5% unique ratio or ≤ 10 unique values)
binary	Booleans, or any column with exactly 2 unique non-null values
temporal	Date, Datetime, or Duration columns
high_cardinality	Likely identifiers, UUIDs, or free text — statistical summary skipped

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Using as a Python Library

The core functions are also importable directly:

from eda_mcp import load_file, classify_column, get_summary, generate_markdown_report

df = load_file("data/sales.parquet")
summary = get_summary(df["revenue"])
generate_markdown_report(df, "data/sales.parquet", "output/")

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Example Prompts

Once connected to Claude:

Analyze this dataset: /path/to/data.csv

What columns in sales.parquet have missing values?

Is age correlated with income in this file?

Generate a full EDA report for customers.xlsx

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Requirements

• Python 3.11+
• Dependencies are installed automatically via uvx or pip

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License

MIT