"Hypothesis" matching MCP servers:

MCP server for the Hypothesis annotation service, enabling Claude to search, create, and manage annotations, as well as read and discover PDFs from your library.

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.

Provides verified statistical inference and hypothesis testing tools, including t-tests, effect sizes, power analysis, and multiple comparisons correction, with assumption checks and citations.

Provides a tool for dynamic and reflective problem-solving by breaking complex problems into manageable steps with support for revision, branching, and hypothesis generation.

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.

An MCP server that provides an LLM with access to R's statistical toolkit including hypothesis tests, regression, psychometrics, survival analysis, time series, and plots through a persistent R session.

A persistent, self-revising hypothesis DAG for agentic R&D, exposed as an MCP server. It enables agents to structure working knowledge as a directed acyclic graph of hypotheses, with automatic write-back belief revision and cascading pruning based on evidence.

Enables hierarchical problem analysis using logic trees with MECE validation, hypothesis generation, and feasibility assessment for structured decision-making.

A Python MCP server for hypothesis-driven data analysis, managing analysis designs, data catalogs, and review workflows through Claude Code or any MCP-compatible client.

Enables AI agents to query verifiable physical-world ground truth for any point on Earth — water availability, seismic and space-weather hazard, ground stability, and resource indications — with every answer backed by a Bitcoin-anchored provenance record that can be independently verified. It also lets agents check whether an Earth-science hypothesis has already been tested, list documented nulls and retractions, and run bounded controlled tests that return UNTESTABLE rather than fabricate a result.

MCP server that integrates a 1200-paper RAG database with six tools to support research workflows across stages like hypothesis, experiment, statistics, and writing. It routes requests to specialized skills and real-time frontier searches to provide evidence-grounded research mentoring.

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.

A virtual statistician MCP server that provides real statistical methods—design of experiments, hypothesis testing, regression, SPC, MSA—via tested Python libraries, with plain-language interpretations and plotted outputs.

A statistical analysis MCP server offering 30 tools for descriptive statistics, hypothesis tests, regression, and time series, all returning Markdown reports with automatic interpretations to enable AI agents to perform comprehensive data analysis.

Provides a virtual statistician for AI agents, offering real statistical methods such as design of experiments, hypothesis testing, regression, and process control. It includes an advisor tool to recommend appropriate analyses and generates plain-language interpretations of results.

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

Provides MCP wrappers for Allen AI's Asta research tools, enabling AI agents to manage local document collections and perform deep literature searches. It supports automated report generation, PDF text extraction, and research hypothesis synthesis.

Enables comprehensive statistical analysis including descriptive statistics, hypothesis testing, regression, and more via a FastMCP-based API.