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Stats Compass MCP
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Stats Compass MCP

stats - compass - mcp is an MCP tool that can transform an LLM into a data analyst, providing various data science functions such as data loading, cleaning, exploratory analysis, visualization, and machine learning workflows.
Research and dataDeveloper tools#Data analysis#Machine learning#Data visualization#Data cleaning.Python
2.5 points
6.9K
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Overview

Installation

Tools List

Content Details

Alternatives

What is Stats Compass MCP?

Stats Compass MCP is a Model Context Protocol server that allows your AI assistants (such as Claude, GitHub Copilot, etc.) to directly access data science tools. You can use natural language instructions to let AI load CSV/Excel files, clean data, run statistical analyses, create visualizations, and even train machine learning models without writing any code.

How to use Stats Compass MCP?

After installing Stats Compass MCP, simply add the prefix "Use stats compass to..." at the beginning of your conversation in your AI client (such as Claude Desktop, VS Code Copilot), and then describe the data analysis task you want. AI will automatically use the corresponding tools to handle your request.

Applicable scenarios

Suitable for users such as data analysts, researchers, business personnel, and students who need to quickly analyze data but don't want to write complex code. Particularly suitable for scenarios such as exploratory data analysis (EDA), data cleaning and preprocessing, creating visual reports, running statistical tests, and building simple machine learning models.

Main features

Data loading
Support loading CSV and Excel files, with built - in sample datasets, and list and manage multiple DataFrames.
Data cleaning
Automatically handle missing values (delete, fill), remove duplicates, detect and handle outliers.
Data transformation
Data filtering, grouping and aggregation, data pivoting, encoding conversion, adding calculated columns.
Exploratory analysis
Data descriptive statistics, correlation analysis, hypothesis testing, data quality assessment.
Data visualization
Create various charts such as histograms, scatter plots, bar charts, ROC curves, and confusion matrices.
Machine learning
Machine learning workflows such as classification, regression, and time series prediction.
Remote server mode
Support Docker deployment and multi - client connection, and provide file upload and result download functions.
Advantages
No programming required: Complete complex data analysis tasks through natural language instructions.
Easy integration: Seamlessly integrate with mainstream AI clients such as Claude Desktop and VS Code Copilot.
Comprehensive functions: Cover the complete data analysis process from data loading to machine learning.
User - friendly: Provide a file upload interface and result download link, with simple and intuitive operations.
Flexible deployment: Support both local installation and remote server deployment methods.
Limitations
Dependent on AI clients: Need to be used with supported AI assistants.
File size limit: The default maximum upload file is 50MB.
Limited compatibility with some AI clients: For example, GPT/Gemini are only partially supported.
Requires a specific instruction prefix: Must start with "Use stats compass to..." to trigger the tool.
Limited advanced customization: Compared with direct programming, the degree of customization is limited to a certain extent.

How to use

Install Stats Compass MCP
Install the stats - compass - mcp package via pip.
Configure the AI client
Select the corresponding installation command according to the AI client you are using.
Restart the client
Restart your AI client to load the Stats Compass MCP tool.
Start using
Start data analysis by using the prefix "Use stats compass to..." in the AI conversation.

Usage examples

Sales data analysis
Analyze sales data to understand sales trends and customer behavior.
Customer churn prediction
Use machine learning to predict which customers are likely to churn.
A/B test analysis
Analyze the results of an A/B test to determine which version performs better.
Data quality check
Check the quality issues in the dataset and automatically fix them.

Frequently Asked Questions

Why isn't my AI assistant using the Stats Compass tool?
How to upload files to the remote server?
Which file formats are supported?
How to download the analysis results?
Which AI clients does Stats Compass MCP support?
How about data security?
How to handle large datasets?

Related resources

GitHub repository
Source code and latest updates of Stats Compass MCP.
PyPI package page
Python package installation page and version history.
Model Context Protocol documentation
Official documentation and specifications of the MCP protocol.
Claude Desktop configuration guide
Installation and configuration instructions for Claude Desktop.
Contribution guide
How to contribute code to Stats Compass MCP.

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "stats-compass": {
      "command": "uvx",
      "args": ["mcp-proxy", "--transport", "streamablehttp", "http://localhost:8000/mcp"]
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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