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.

This project is an MCP server for managing memory text files, helping AI models like Claude maintain context between conversations. It provides functions to add, search, delete, and list memories, supporting exact matching operations based on substrings. It is designed to store memories in simple text files, similar to ChatGPT's memory mechanism, and triggers memory storage through prompts and training.

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.

The Unsloth MCP Server is a server for efficiently fine-tuning large language models. Through optimized algorithms and 4-bit quantization technology, it achieves a 2-fold increase in training speed and an 80% reduction in video memory usage, supporting multiple mainstream models.

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.

LudusMCP is a model context protocol server for managing the Ludus laboratory environment based on natural language commands. It provides functions for deploying, configuring, and managing virtualized training environments and supports connecting to the Ludus server through WireGuard VPN or SSH tunnel.

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.

Create a training project for 100 MCP servers

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.

Implementation of the MCP server for the Hevy API, providing a toolset for interacting with the Hevy fitness platform, including functions such as training record and routine management.

An MCP service that helps technical coaches create structured learning hour courses. It generates 60-minute technical training courses with code examples and interactive whiteboards through the 4C learning model.

AWorld is a multi - agent system framework aiming to bridge the gap between theoretical MAS capabilities and practical applications, providing a full - set solution from single - agent to multi - agent collaboration/competition. The project supports scenarios such as browser/mobile operations and GAIA benchmark testing, adopts a client - server architecture, integrates a rich toolchain, and includes performance evaluation and training functions.

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.

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.