The Linear Regression MCP project demonstrates an end-to-end machine learning workflow using Claude and the Model Context Protocol (MCP), including data preprocessing, model training, and evaluation.

A comprehensive chess analysis MCP server that integrates Stockfish engine evaluation, thematic analysis, opening databases, puzzle training, and game visualization, providing advanced chess analysis and game improvement functions.

A TypeScript server that connects Hevy fitness data with language models and provides tools for fitness history, training progress, and personal records through the MCP protocol.

This is an MCP server that provides a standardized interface for Scikit-learn models, supporting functions such as model training, evaluation, data preprocessing, and persistence.

This project demonstrates the training of a linear regression model using Claude and the Model Context Protocol (MCP) for an end-to-end machine learning workflow. Users only need to upload a CSV dataset, and the system can automatically complete the entire process of data preprocessing, model training, and evaluation (RMSE calculation).

The MCP tool is a tool for managing model context in GitHub repositories, supporting version tracking, dataset management, performance recording, and documentation of training configurations.

This project is an integration tool between the Strava API and the Model Context Protocol (MCP) SDK, used to analyze training data and provide personalized recommendations. It supports functions such as training activity analysis, automatic token update, and API request rate limit.

A Model Context Protocol server that provides access to the Whoop API, supporting queries for health data such as workout cycles, recovery status, and training loads.

This project is an integration solution of the Strava API and the Model Context Protocol (MCP) SDK, used to analyze training data and provide personalized recommendations.

This project provides an interface for AI assistants to access Haskell documentation. By retrieving authoritative documentation on Hackage in real - time, it solves the problem of insufficient training data for AI in the Haskell field and improves the accuracy of code generation and explanation.

This project is a research on automated medical coding, providing code for training and evaluating medical coding models on the MIMIC - III and MIMIC - IV datasets, including the implementation of multiple models and the division of new datasets.