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.
Quickstart
Run instantly with no install step:
uvx eda-mcp
Or install permanently:
pip install eda-mcp
Connecting to Claude Desktop
Add this to your claude_desktop_config.json:
Mac: ~/Library/Application Support/Claude/claude_desktop_config.jsonWindows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"eda-mcp": {
"command": "uvx",
"args": ["eda-mcp"]
}
}
}
Restart Claude Desktop. The tools will appear automatically.
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. |
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.
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 |
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/")
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
Requirements
- Python 3.11+
- Dependencies are installed automatically via
uvxorpip
License
MIT
