Building on the foundation of the Gemini MCP server, this post explores the advanced features that transform a simple AI wrapper into a sophisticated multi-agent orchestration platform. We'll examine the debate system with its voting mechanisms, the swarm architecture with specialized agents, and the complex information flows that enable AI-to-AI collaboration.
This post documents the architecture and implementation of a production-ready MCP (Model Context Protocol) server that wraps Google's Gemini CLI, enabling Claude Code to leverage Gemini as an adjacent AI for deep analysis, code review, and structured AI-to-AI debates.
Claude Code's Model Context Protocol (MCP) enables powerful integrations with external tools, but loading all MCP servers simultaneously can consume 100k+ tokens of your context window. This post explores a profile-based architecture that optimizes token usage while maintaining full capabilities when needed.
claude-flow enables orchestrated multi-agent swarms within Claude Code, allowing complex tasks to be broken down and executed in parallel by specialized agents. This post explores the architecture, practical patterns, and production configurations for AI-to-AI collaboration.