AI-powered IDEs and copilots have improved code scaffolding and suggestions but struggle with debugging due to incomplete data contexts leading to inaccurate fixes. Multiplayer MCP Server addresses this by providing full-stack session recordings that integrate frontline screens, backend data, user actions, and team annotations into a single, comprehensive timeline. This approach minimizes guesswork and enables AI tools like Cursor, Claude Code, and Copilot to produce precise and actionable code with minimal prompting. The system supports varied configuration options to suit different debugging workflows, allowing for enriched session recordings that facilitate accurate problem-solving, feature development, and system exploration. By delivering a complete data context, Multiplayer MCP enhances the capability of AI assistants, reducing errors and improving the efficiency of coding and debugging processes.