Create before-and-after code examples for learning topics to illustrate concepts like 'Feature Envy' or 'DRY Principle' in a specified programming language, enhancing technical coaching sessions.
Manually initiate a learning cycle to analyze patterns, improve machine learning models, and enhance predictive capabilities within the MCP Self-Learning Server.
Generate structured learning paths for programming libraries based on your experience level, providing progressive topics and resources for effective skill development.
Analyze interactions to generate personalized learning insights and actionable recommendations, driving continuous improvement and informed decision-making through pattern recognition and machine learning.
A learning-focused MCP server that demonstrates how to build arithmetic tools for AI assistants, currently featuring addition functionality with structured input/output.
An MCP server that connects to the Resource Hub, allowing centralized configuration and management of tools and resources across different MCP environments.
Manages resource allocation, MCP server lifecycle, and Kubernetes workers in cortex automation systems. Provides tools for requesting/releasing job resources, starting/stopping/scaling MCP servers, and provisioning/destroying burst workers with TTL management.