A
licenseA
qualityC
maintenanceA Model Context Protocol server that provides basic mathematical and statistical functions to LLMs, enabling them to perform accurate numerical calculations through a simple API.
13
35 npm
191
MIT
MCP Data Wrangler
Enables data wrangling operations including data aggregation and descriptive statistics through Python, allowing for preprocessing, transformation, and analysis of data in standardized ways.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Data Wrangler calculate summary statistics for my sales dataset"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
Related MCP server: Filesystem MCP Server
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:
git clone https://github.com/yourusername/mcp-data-wrangler.git
cd mcp-data-wranglerYou can launch the MCP inspector via npm:
npx @modelcontextprotocol/inspector uv --directory=src/mcp_data_wrangler run mcp-data-wranglerUpon 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:
{
"data-wrangler": {
"command": "uv",
"args": [
"--directory",
"/path/to/mcp-data-wrangler",
"run",
"mcp-data-wrangler"
]
}
}Development
Create and activate a virtual environment:
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activateInstall dependencies:
pip install -e ".[dev]"Run tests:
pytest -s -v tests/License
Available Tools
16 toolsdata_countC
Number of non-null elements for each column
Inputs: input_data_file_path.
data_estimated_sizeC
Estimated size of the input data
Inputs: input_data_file_path, unit.
data_maxC
Maximum values for each column
Inputs: input_data_file_path.
data_max_horizontalC
Maximum values across columns for each row
Inputs: input_data_file_path.
data_meanC
Mean values for each column
Inputs: input_data_file_path.
data_mean_horizontalC
Mean values across columns for each row
Inputs: input_data_file_path.
data_medianC
Median values for each column
Inputs: input_data_file_path.
data_minC
Minimum values for each column
Inputs: input_data_file_path.
data_min_horizontalC
Minimum values across columns for each row
Inputs: input_data_file_path.
data_productC
Product values for each column
Inputs: input_data_file_path.
data_quantileC
Quantile values for each column
Inputs: input_data_file_path (required), quantile (required), interpolation.
data_schemaD
Data schema of the input data
Inputs: input_data_file_path.
data_shapeD
Data shape of the input data
Inputs: input_data_file_path.
data_stdC
Standard deviation values for each column
Inputs: input_data_file_path (required), ddof.
data_varC
Variance values for each column
Inputs: input_data_file_path (required), ddof.
describe_dataC
Summary statistics of the input data
Inputs: input_data_file_path, interpolation, percentiles.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
B3/5.0
Scored across 16 tools
Disambiguation5/5
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.
Naming Consistency5/5
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.
Tool Count4/5
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.
Completeness5/5
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.
Maintenance
ActivityInactive
ResponsivenessOutdated data
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