50+ pandas-powered tools for data loading, cleaning, visualization, and ML workflows
MCP Server: io.github.oogunbiyi21/stats-compass
The io.github.oogunbiyi21/stats-compass MCP server provides 50+ pandas-powered tools for data-centric workflows. It supports tasks across data loading, cleaning, visualization, and machine learning (ML) workflows, exposing these capabilities through MCP for use by connected applications.
🛠️ Key Features
50+ tools
Built on pandas
Covers data loading
Includes data cleaning
Provides data visualization
Supports ML workflows
🚀 Use Cases
Automating data loading and cleaning steps for analysis pipelines
Generating visualizations as part of exploratory or reporting workflows
Running machine learning workflow tasks via MCP-enabled tool calls
⚡ Developer Benefits
Uses a pandas-based toolset covering multiple stages of data workflows
Provides an MCP interface to multiple data science tools
⚠️ Limitations
Tool coverage is described only at a high level (loading, cleaning, visualization, ML); specific tool count and individual tool details are not provided in the available data.
claude mcp add stats-compass -- uvx stats-compass-mcp run
Note: The first connection may fail while uvx downloads the package. If this happens, disable and re-enable Stats Compass in your MCP settings — subsequent connections will be instant.
Restart your client and start asking questions about your data.
What Can It Do?
Demo: Cleaning and transforming data
Category
Examples
Data Loading
Load CSV/Excel, sample datasets, list DataFrames
Cleaning
Drop nulls, impute, dedupe, handle outliers
Transforms
Filter, groupby, pivot, encode, add columns
EDA
Describe, correlations, hypothesis tests, data quality
Visualization
Histograms, scatter, bar, ROC curves, confusion matrix
ML Workflows
Classification, regression, time series forecasting
Run stats-compass-mcp list-tools to see all available tools.
How to Prompt
Start your message with "Use stats compass to..." — this tells the AI to use the Stats Compass tools instead of trying to write code or use other methods.
code
Use stats compass to load ~/Downloads/sales.csv and run EDA on it
Use stats compass to find my CSV files in Downloads
Use stats compass to clean the dataset and handle missing values
Use stats compass to create a histogram of the price column
Use stats compass to test if there's a significant difference in scores between group A and B
Use stats compass to train a classification model to predict churn
Tip: Without this prefix, some AI clients may try to write Python code or use shell commands instead of the Stats Compass tools — especially for tasks like finding files on your machine.
Loading Files
Local mode: Start with "Use stats compass to load..." and provide the file path or folder.
code
Use stats compass to load the CSV at ~/Downloads/sales.csv
Use stats compass to find my data files in ~/Documents
Remote/HTTP mode: Use the upload feature (see below).
Remote Server Mode
For Docker deployments or multi-client setups:
bash
stats-compass-mcp serve --port 8000
File Uploads
When running remotely, users can upload files via browser:
code
You: I want to upload a file
AI: Open this link to upload: http://localhost:8000/upload?session_id=abc123
[Upload in browser]
You: I uploaded sales.csv
AI: ✅ Loaded sales.csv (1,000 rows × 8 columns)
Downloading Results
Export DataFrames, plots, and trained models:
code
You: Save the cleaned data as a CSV
AI: ✅ Saved. Download: http://localhost:8000/exports/.../cleaned_data.csv
