Show notes
Naseem Al-Naji is the co-founder of MCPcat.io and the creator of Opal — a builder with deep roots in privacy-first developer tooling. In this conversation, he breaks down why MCP servers have become a black box in production, and how MCPcat gives teams X-ray vision into how agents and users actually behave.What we get into:🐱 What MCPcat Is — Open-source analytics and live debugging built specifically for MCP servers🎬 Session Replay — Watch an agent's full journey through your server, tool call by tool call🎯 Agent Intent & Goals — Understand "why" a tool was called, not just that it was🔍 Trace Debugging — Find exactly where agents and users get stuck or confused🚨 Catching Hallucinations — How issue tracking surfaces when an LLM goes off the rails🔒 Privacy-First by Design — Client-side redaction so sensitive data never leaves your environment⚡ One-Line Integration — Python, TypeScript, and Go SDKs that drop into existing stacks📊 Works With Your Stack — Native support for OpenTelemetry, Datadog, and Sentry🚀 The Future of MCP — Where agent observability and the MCP ecosystem are headingIf you build, ship, or maintain MCP servers — or you're trying to figure out why your AI agents misbehave in production — this one's for you.🔔 Subscribe, like, and share for more conversations on agentic AI:▶️ YouTube: https://www.youtube.com/@AAIFAgenticConversations🎧 Spotify: https://open.spotify.com/show/033rZZJrQOVSSmhcStFhZA?si=rUNjFuNqRvGvAEWwqms7TALinks & Resources:🐱 MCPcat: https://mcpcat.io💻 MCPcat on GitHub: https://github.com/mcpcat👤 Naseem on LinkedIn: https://www.linkedin.com/in/naseem-al-naji🐙 Naseem on GitHub: https://github.com/naji247Timestamps:[00:00] Intro[01:41] MCP Needs Gatekeepers[06:32] Measuring MCP Success[13:57] MCPAT Feature Rollouts[18:50] MCP Server Query Optimization[26:48] UI Design Shift[29:14] MCP Server Design Choices[33:51] User Journey Traceability[40:40] Agent Experience Evaluation[45:23] AI Model Improvement Strategies#MCP #AIAgents #Observability
