"Hypothesis" matching MCP servers:

Enables statistical A/B testing by computing Welch's two-sample t-test, Satterthwaite degrees of freedom, and two-tailed p-values, alongside time-series forecasting, anomaly detection, regression, and PCA through MCP tools.

Enables AI agents to perform reproducible, verifiable statistical analysis through 25 deterministic tools for descriptive statistics, hypothesis testing, regression, clustering, time-series forecasting, and Chinese-labeled plotting.

Structured reasoning MCP server that decomposes problems into atomic steps (premise, reasoning, hypothesis, verification, conclusion) with confidence scoring, live visualization, and approval feedback.

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.

Enables agents to forecast numerical trends with zero-dependency Holt linear exponential smoothing, multi-step horizons, variance confidence bands, and supporting statistical anomaly detection, regression, hypothesis testing, and PCA.

Enables zero-dependency statistical analysis of numerical and time-series streams, flagging outliers via standard Z-score, modified median absolute deviation (MAD), and Tukey IQR fences. Also supports trend forecasting with Holt linear smoothing, multivariate gradient-descent regression, Welch's t-test hypothesis testing, and Power Iteration PCA dimensionality reduction through a native MCP stdio interface.

Enables agents to detect outliers in numerical streams using multiple robust statistical strategies and to perform related analytics such as forecasting, regression, hypothesis testing, and dimensionality reduction via MCP.

An MCP server that enforces a scientific-method loop for AI-driven machine learning experiments, with hypothesis gating, diagnostics, and data forensics.