Enables MCP clients such as Claude Desktop, Cursor, and Windsurf to run two-sample Welch's t-tests that compute Satterthwaite degrees of freedom and two-tailed p-values for A/B test comparisons. It also exposes zero-dependency statistical primitives including Holt linear forecasting, Z-score/IQR anomaly detection, gradient-descent regression, and power-iteration PCA, all using only the Python standard library.
Enables agents to run zero-dependency statistical modeling and data analysis through MCP, including multivariate linear regression via gradient descent, anomaly detection, time-series forecasting, hypothesis testing, and PCA dimensionality reduction.
Enables agents to run statistics and data-science computations over numerical streams using only the Python standard library, including Z-score/IQR anomaly detection, Holt linear forecasting, Welch's t-test hypothesis evaluation, gradient-descent multivariate regression, and power-iteration SVD/PCA dimensionality reduction. Exposes these capabilities over JSON-RPC 2.0 stdio so clients like Claude Desktop, Cursor, and Windsurf can project high-dimensional feature vectors into principal components without any external dependencies.
MCP server for AIRAS, an open-source research automation platform. It provides tools for paper search, retrieval, hypothesis generation, experiment execution, and paper writing, enabling automated or interactive research directly from MCP clients.
