Gemini CLI Orchestrator MCP

# Comprehensive Analysis Context ## Original Goal Synthesize a comprehensive understanding of the gemini-cli-orchestrator architecture, focusing on how it successfully implements pure metaprompting principles and serves as an effective teacher model for AI collaboration ## Analysis Steps Completed Step 1: Analyzed core metaprompting philosophy and teacher model architecture - Understanding the fundamental principle of 'Don't build intelligence into the system. Build prompts that elicit intelligence from the agent' and how it's implemented as a pure guidance system with no execution logic. Step 2: Analyzed metaprompting patterns and intelligence elicitation - Understanding how the system uses specific language patterns, cognitive frameworks (ReAct loops, Progressive Understanding), and strategic guidance to teach agents to think systematically and leverage Gemini's strengths. ## Key Insights from Each Step ### Step 1: Core Philosophy & Architecture - Pure metaprompting approach: System provides NO execution logic, only returns metaprompts - Teacher model that guides without executing: "Don't build intelligence into the system. Build prompts that elicit intelligence from the agent" - Stateless guidance system with no workflow storage or file processing - Differs from traditional wrappers by teaching HOW to interact rather than abstracting interaction away - Four core tools: analysis_workflow, collaboration_step, react_loop, insight_synthesis ### Step 2: Intelligence Elicitation Patterns - Language patterns emphasize "HOW" over "WHAT" - teaches methodology, not just commands - Embedded cognitive frameworks: ReAct loops (Thought→Action→Observation→Reflection), Progressive Understanding (5-stage analysis), Systems Thinking - Strategic guidance on leveraging Gemini's strengths: 1M+ context window, security analysis, pattern recognition - Moves beyond prescriptive instructions to intelligence-eliciting guidance through reflective questions and templates - Meta-cognitive awareness: includes bias detection and self-reflection prompts ## Patterns Identified - Consistent "teaches you HOW to..." language pattern across all tools - Structured formatting (markdown, templates, frameworks) to organize complex guidance - Emphasis on hypothesis-driven, iterative analysis - Integration of proven cognitive frameworks from psychology and AI research - Focus on maximizing Gemini's unique capabilities rather than generic AI interaction ## Questions That Emerged - How does this pure metaprompting approach scale to more complex workflows? - What are the limitations of stateless guidance without workflow persistence? - How do agents handle when the guidance doesn't match their specific context? - What happens when agents need to deviate from the suggested frameworks? ## Areas Needing Deeper Investigation - Practical implementation examples from real-world usage - Comparison with other metaprompting systems - Success metrics and evaluation criteria - Integration patterns with different AI agents and IDEs EOF < /dev/null