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data-analysis-mcp

Runs locallyMITUpdated on Mar 20, 20267GitHub stars

A set of statistical analysis tools for industrial and time-series data, documented in Chinese, built on FastAPI with FastAPI-MCP so the same endpoints serve as a REST API and as an MCP server over streamable HTTP on a local port.

InstallRepository Checked on Nov 12, 2026

What it does

A set of statistical analysis tools for industrial and time-series data, documented in Chinese, built on FastAPI with FastAPI-MCP so the same endpoints serve as a REST API and as an MCP server over streamable HTTP on a local port. The tools compute correlation between two series, run ADF, PP and KPSS stationarity tests, describe distributions, detect outliers, test Granger causality between two or more variables, measure time-series similarity with dynamic time warping, and make short forecasts with polynomial trends or exponential smoothing. Data is passed in the request as arrays of numbers. It runs locally with Python after installing the requirements.

Capabilities

  • Calculate Pearson, Spearman or Kendall correlation
  • Run stationarity tests on a time series
  • Detect outliers in a data series
  • Test causality between variables
  • Forecast a time series with trend or smoothing models

Other MCP servers and skills for Data analysis