MCP Tabular Data Analysis Server

A Model Context Protocol (MCP) server that provides powerful tools for analyzing numeric and tabular data. Works with CSV files and SQLite databases.

Features

Core Tools

Tool	Description
`list_data_files`	List available CSV and SQLite files in the data directory
`describe_dataset`	Generate comprehensive statistics for a dataset (shape, types, distributions, missing values)
`detect_anomalies`	Find outliers using Z-score or IQR methods
`compute_correlation`	Calculate correlation matrices between numeric columns
`filter_rows`	Filter data using various operators (eq, gt, lt, contains, etc.)
`group_aggregate`	Group data and compute aggregations (sum, mean, count, etc.)
`query_sqlite`	Execute SQL queries on SQLite databases
`list_tables`	List all tables and schemas in a SQLite database

Advanced Analytics Tools (NEW!)

Tool	Description
`create_pivot_table`	Create Excel-style pivot tables with flexible aggregations
`data_quality_report`	Comprehensive data quality assessment with scores and recommendations
`analyze_time_series`	Time series analysis with trends, seasonality, and moving averages
`generate_chart`	Create visualizations (bar, line, scatter, histogram, pie, box plots)
`merge_datasets`	Join/merge two datasets together (inner, left, right, outer joins)
`statistical_test`	Hypothesis testing (t-test, ANOVA, chi-squared, correlation tests)
`auto_insights`	Automatically discover interesting patterns and insights
`export_data`	Export filtered/transformed data to new CSV files

Installation

Prerequisites

Python 3.10+
uv (recommended) or pip

Install with uv

cd mcp-tabular uv sync

Install with pip

cd mcp-tabular pip install -e .

Usage

Running the Server Directly

# With uv uv run mcp-tabular # With pip installation mcp-tabular

Configure with Claude Desktop

Quick Setup:

Find your Claude Desktop config file:
- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
- Windows: %APPDATA%\Claude\claude_desktop_config.json
- Linux: ~/.config/Claude/claude_desktop_config.json
Add this configuration (replace /Users/kirondeb/mcp-tabular with your actual path):

{ "mcpServers": { "tabular-data": { "command": "/Users/kirondeb/mcp-tabular/.venv/bin/python", "args": [ "-m", "mcp_tabular.server" ] } } }

Restart Claude Desktop completely (quit and reopen)
Test it by asking Claude: "Describe the dataset in data/sample_sales.csv"

📖 For detailed instructions and troubleshooting, see CONNECT_TO_CLAUDE_DESKTOP.md

📝 For example prompts to try, see TEST_PROMPTS.md

Sample Data

The project includes sample data for testing:

data/sample_sales.csv - Sales transaction data
data/sample.db - SQLite database with customers, orders, and products tables

To create the SQLite sample database:

python scripts/create_sample_db.py

Path Resolution

Important: All file paths are resolved relative to the project root directory. This means:

Relative paths like data/sample_sales.csv work from any working directory
Absolute paths also work as expected
The server automatically resolves paths relative to where mcp_tabular is installed

Tool Examples

List Data Files

Discover available data files:

list_data_files()

Returns all CSV and SQLite files in the data directory with metadata.

Describe Dataset

Get comprehensive statistics about a dataset:

describe_dataset(file_path="data/sample_sales.csv")

Returns shape, column types, numeric statistics (mean, std, median, skew, kurtosis), categorical value counts, and a sample preview.

Detect Anomalies

Find outliers in numeric columns:

detect_anomalies( file_path="data/sample_sales.csv", column="total_sales", method="zscore", threshold=3.0 )

Supports zscore and iqr methods.

Compute Correlation

Analyze relationships between numeric columns:

compute_correlation( file_path="data/sample_sales.csv", method="pearson" )

Returns full correlation matrix and top correlations ranked by strength.

Filter Rows

Filter data based on conditions:

filter_rows( file_path="data/sample_sales.csv", column="category", operator="eq", value="Electronics" )

Operators: eq, ne, gt, gte, lt, lte, contains, startswith, endswith

Group & Aggregate

Group data and compute aggregations:

group_aggregate( file_path="data/sample_sales.csv", group_by=["category", "region"], aggregations={"total_sales": ["sum", "mean"], "quantity": ["count"]} )

Query SQLite

Execute SQL queries on databases:

query_sqlite( db_path="data/sample.db", query="SELECT * FROM customers WHERE lifetime_value > 1000" )

List Tables

Explore SQLite database structure:

list_tables(db_path="data/sample.db")

Advanced Analytics Examples

Create Pivot Table

Create Excel-style pivot tables for business analysis:

create_pivot_table( file_path="data/sample_sales.csv", index=["region"], columns=["category"], values="total_sales", aggfunc="sum" )

Data Quality Report

Get a comprehensive quality assessment before analysis:

data_quality_report(file_path="data/sample_sales.csv")

Returns completeness score, duplicate detection, outlier analysis, and an overall quality grade (A-F).

Time Series Analysis

Analyze trends and seasonality in time-based data:

analyze_time_series( file_path="data/sample_sales.csv", date_column="order_date", value_column="total_sales", freq="M", include_forecast=True )

Generate Charts

Create visualizations returned as base64 images:

generate_chart( file_path="data/sample_sales.csv", chart_type="bar", x_column="category", y_column="total_sales", title="Sales by Category" )

Supported chart types: bar, line, scatter, histogram, pie, box

Merge Datasets

Combine data from multiple sources:

merge_datasets( file_path_left="data/orders.csv", file_path_right="data/customers.csv", on=["customer_id"], how="left" )

Statistical Testing

Perform hypothesis tests for data science:

statistical_test( file_path="data/sample_sales.csv", test_type="ttest_ind", column1="total_sales", group_column="region", alpha=0.05 )

Supported tests: ttest_ind, ttest_paired, chi_squared, anova, mann_whitney, pearson, spearman

Auto Insights

Automatically discover interesting patterns:

auto_insights(file_path="data/sample_sales.csv")

Returns insights about correlations, outliers, skewed distributions, missing data, and more.

Export Data

Export filtered data to a new CSV:

export_data( file_path="data/sample_sales.csv", output_name="electronics_sales", filter_column="category", filter_operator="eq", filter_value="Electronics", sort_by="total_sales", sort_ascending=False )

Development

Run Tests

uv run pytest

Project Structure

mcp-tabular/ ├── src/ │ └── mcp_tabular/ │ ├── __init__.py │ └── server.py # Main MCP server implementation ├── data/ │ ├── sample_sales.csv # Sample CSV data │ └── sample.db # Sample SQLite database ├── scripts/ │ └── create_sample_db.py ├── pyproject.toml ├── claude_desktop_config.json └── README.md

License

MIT

MCP Tabular Data Analysis Server

MCP Tabular Data Analysis Server

Features

Core Tools

Advanced Analytics Tools (NEW!)

Installation

Prerequisites

Install with uv

Install with pip

Usage

Running the Server Directly

Configure with Claude Desktop

Sample Data

Path Resolution

Tool Examples

List Data Files

Describe Dataset

Detect Anomalies

Compute Correlation

Filter Rows

Group & Aggregate

Query SQLite

List Tables

Advanced Analytics Examples

Create Pivot Table

Data Quality Report

Time Series Analysis

Generate Charts

Merge Datasets

Statistical Testing

Auto Insights

Export Data

Development

Run Tests

Project Structure

License

Resources

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