50+ pandas-powered tools for data loading, cleaning, visualization, and ML workflows
{
"mcpServers": {
"io-github-oogunbiyi21-stats-compass": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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50+ pandas-powered tools for data loading, cleaning, visualization, and ML workflows
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Last commit 0 days ago. 12 stars.
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Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
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pip install stats-compass-mcp
stats-compass-mcp install --client claude
stats-compass-mcp install --client vscode
claude mcp add stats-compass -- uvx stats-compass-mcp run
Restart your client and start asking questions about your 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.
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.
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.
Local mode: Start with "Use stats compass to load..." and provide the file path or folder.
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).
For Docker deployments or multi-client setups:
stats-compass-mcp serve --port 8000
When running remotely, users can upload files via browser:
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)
Export DataFrames, plots, and trained models:
You: Save the cleaned data as a CSV
AI: ✅ Saved. Download: http://localhost:8000/exports/.../cleaned_data.csv
VS Code (native HTTP support):
{
"servers": {
"stats-compass": { "url": "http://localhost:8000/mcp" }
}
}
Claude Desktop (via mcp-proxy):
{
"mcpServers": {
"stats-compass": {
"command": "uvx",
"args": ["mcp-proxy", "--transport", "streamablehttp", "http://localhost:8000/mcp"]
}
}
}
docker run -p 8000:8000 -e STATS_COM
... [View full README on GitHub](https://github.com/oogunbiyi21/stats-compass-mcp#readme)