"Matrix" matching MCP servers:

Bridges a Matrix room with Claude Code's claude/channel feature, enabling chat from Matrix to interact with a running Claude Code session.

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.

Enables agents to run zero-dependency statistical analysis natively over MCP, including Power Iteration PCA/SVD dimensionality reduction, Holt linear forecasting, modified Z-score and Tukey IQR anomaly detection, Welch's t-test, and multivariate gradient-descent regression on numerical data streams. Projects high-dimensional feature vectors into principal component representations without any external libraries.

An MCP server that provides access to SymPy's symbolic mathematics library for advanced algebraic computations. It enables users to perform complex tasks such as symbolic simplification, calculus, equation solving, matrix operations, and number theory.

Enables Claude Code to communicate with an OpenClaw bot via Matrix, allowing you to send messages and read replies using natural language.

Enables local offline machine learning workflows, including CSV profiling and cleaning, classifier and neural network training, hyperparameter optimization, evaluation metrics, confusion matrix plots, Streamlit dashboards, and HTML reports through MCP tools.

Accepts a coupling matrix (covariance, correlation, or precision) and returns zone classifications, optimal factorization strategy, and calibrated risk predictions. Answers: which variable dependencies are load-bearing and which can be safely ignored?

RegexForge gives AI agents a reliable way to get a regex without asking an LLM to hallucinate one. Pass in labeled examples (strings that should match, strings that shouldn't) plus an optional description; get back the regex, a proof matrix showing it handles every example, and a backtracking-risk audit flagging catastrophic-backtracking patterns. Pure symbolic synthesis over a template bank with