{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/d4d0003f06fa4327b46ba5c081bdf835\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/d4d0003f06fa4327b46ba5c081bdf835-d902fc6ca6ac1b0c.gif","duration":493.26,"title":"Introducing TraceMind MCP Server: Revolutionizing AI Agent Evaluation 🚀","description":"Hello everyone! In this video, I’m excited to introduce you to the TraceMind MCP server, a production-ready tool designed to tackle real AI agent evaluation challenges. It’s part of my TraceMine ecosystem, which includes projects like TraceWordA and SmallTrace, enabling users to generate custom datasets and evaluate AI models efficiently. I demonstrate how to configure the MCP server and use its tools to analyze leaderboard data, focusing on cost and accuracy. I encourage you to explore the links provided for more details and to try out the MCP server for your own evaluations."}