MCP Data Wrangler
# mcp-data-wrangler: MCP server for Data Wrangling
## Overview
This is a Model Context Protocol server for Data Wrangling, providing a standardized interface for data preprocessing, transformation, and analysis tasks. It enables seamless integration of data wrangling operations into the MCP ecosystem.
## Features
* Data aggregation
* Descriptive statistics
## Run this project locally
This project is not yet set up for ephemeral environments (e.g. `uvx` usage). Run this project locally by cloning this repo:
```bash
git clone https://github.com/yourusername/mcp-data-wrangler.git
cd mcp-data-wrangler
```
You can launch the MCP inspector via npm:
```bash
npx @modelcontextprotocol/inspector uv --directory=src/mcp_data_wrangler run mcp-data-wrangler
```
Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
OR Add this tool as a MCP server:
```json
{
"data-wrangler": {
"command": "uv",
"args": [
"--directory",
"/path/to/mcp-data-wrangler",
"run",
"mcp-data-wrangler"
]
}
}
```
## Development
1. Create and activate a virtual environment:
```bash
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
```
2. Install dependencies:
```bash
pip install -e ".[dev]"
```
3. Run tests:
```bash
pytest -s -v tests/
```
## [License](LICENSE)
TDQS
Scored across 16 tools
Every tool has a clearly distinct purpose focused on specific statistical calculations or data structure analysis. The tools are well-differentiated by their mathematical functions (mean, median, std, var, etc.) and orientation (column vs. horizontal), with no ambiguity about which tool to use for each operation.
All tools follow a consistent 'data_' prefix with descriptive suffixes that clearly indicate their function. The naming pattern is uniform throughout (snake_case, descriptive terms), making it easy to understand what each tool does from its name alone.
Sixteen tools is slightly high but reasonable for a comprehensive data analysis toolkit. The server covers extensive statistical operations, which justifies the count, though some tools like data_product might be less commonly used compared to core statistics.
The toolset provides complete coverage for data wrangling and statistical analysis, including descriptive statistics (mean, median, std, var), data structure inspection (schema, shape), and specialized calculations (quantiles, horizontal operations). There are no obvious gaps for this domain.