Monitor and analyze AI agent performance metrics including token usage, execution time, validation scores, security assessments, and test coverage to identify areas for improvement.
Monitor and evaluate AI agent performance by tracking key metrics such as tokens used, time elapsed, validation score, security score, and test coverage to optimize efficiency and quality.
Set up an AI agent workspace by generating template files and configuring project context based on specified project path, name, and optional tech stack.
Bridges Cline to an AI agent system through the Model Context Protocol, enabling message sending, session management, and assistant switching for seamless interaction with AI agents.
Enables searching for AI agents by keywords or categories, allowing users to discover tools like coding agents, GUI agents, or industry-specific assistants across marketplaces.