ThoughtMCP

# Advanced Cognitive Tools **Time**: 30 minutes **Difficulty**: Intermediate to Advanced **Tools Used**: `analyze_systematically`, `think_parallel`, `decompose_problem`, `think_probabilistic`, memory management tools ## Overview ThoughtMCP includes advanced cognitive tools that provide sophisticated reasoning capabilities beyond basic thinking. These tools implement cutting-edge cognitive science research to solve complex problems systematically. ## Systematic Analysis Tools ### analyze_systematically - Framework-Based Problem Solving Automatically selects and applies proven thinking frameworks to structure your analysis. **Scenario**: You need to improve your team's productivity but don't know where to start. **Tool Call:** ```json { "tool": "analyze_systematically", "arguments": { "input": "Our development team is missing deadlines and seems overwhelmed. Productivity has dropped 30% over the last quarter. Team morale is low and we're getting complaints about code quality.", "mode": "auto" } } ``` **What Happens:** - Analyzes problem characteristics - Selects optimal thinking framework - Applies structured analysis steps - Provides systematic recommendations **Expected Response:** ```json { "framework_selection": { "selected_framework": "Root Cause Analysis", "reasoning": "Multiple symptoms suggest underlying systemic issues requiring root cause identification", "confidence": 0.85 }, "systematic_analysis": { "problem_definition": { "primary_symptoms": ["missed deadlines", "low morale", "quality issues"], "impact_assessment": "30% productivity drop with quality degradation" }, "root_cause_analysis": { "potential_causes": [ { "category": "Process Issues", "causes": [ "unclear requirements", "scope creep", "inadequate planning" ], "likelihood": 0.8 }, { "category": "Resource Constraints", "causes": ["understaffing", "skill gaps", "tool limitations"], "likelihood": 0.7 }, { "category": "Communication Problems", "causes": [ "poor stakeholder communication", "team silos", "feedback delays" ], "likelihood": 0.6 } ] }, "recommendations": [ { "priority": "high", "action": "Implement structured requirements gathering and change control process", "expected_impact": "Reduce scope creep by 60%" }, { "priority": "medium", "action": "Conduct skills assessment and provide targeted training", "expected_impact": "Improve code quality and confidence" } ] } } ``` **When to Use:** - Complex problems with multiple factors - Unfamiliar problem domains - Need for structured approach - Team decision-making scenarios ### Different Framework Examples **Design Thinking for Innovation:** ```json { "tool": "analyze_systematically", "arguments": { "input": "We need to create a new feature that helps users manage their time better, but we're not sure what they actually need.", "mode": "auto" } } ``` **Expected Framework**: Design Thinking (Empathize → Define → Ideate → Prototype → Test) **Scientific Method for Hypothesis Testing:** ```json { "tool": "analyze_systematically", "arguments": { "input": "Our website conversion rate dropped 15% after the redesign. We need to figure out what's causing this and how to fix it.", "mode": "auto" } } ``` **Expected Framework**: Scientific Method (Observation → Hypothesis → Experiment → Analysis → Conclusion) ## Parallel Reasoning Tools ### think_parallel - Multi-Stream Reasoning Processes problems through multiple reasoning streams simultaneously for comprehensive analysis. **Scenario**: You're deciding whether to pivot your startup's business model. **Tool Call:** ```json { "tool": "think_parallel", "arguments": { "input": "Should we pivot from B2B SaaS to B2C mobile app? Current B2B revenue is $50k/month but growth has stalled. We have a prototype mobile app that's getting great user feedback but no revenue model yet.", "enable_coordination": true, "context": { "domain": "startup-strategy", "urgency": 0.8, "complexity": 0.9 } } } ``` **What Happens:** - **Analytical Stream**: Logical evaluation of financial data and market metrics - **Creative Stream**: Innovative approaches and unconventional solutions - **Critical Stream**: Risk assessment and assumption challenging - **Synthetic Stream**: Integration of perspectives into holistic view - **Real-time Coordination**: Streams share insights and resolve conflicts **Expected Response:** ```json { "parallel_streams": { "analytical_stream": { "analysis": "B2B revenue of $50k/month provides stability but limited growth. Mobile app has engagement metrics suggesting product-market fit but monetization uncertainty.", "key_metrics": { "b2b_runway": "18 months at current burn rate", "mobile_user_engagement": "4.2/5 rating, 78% daily retention", "pivot_cost": "$200k estimated development and marketing" }, "recommendation": "Gradual transition rather than full pivot" }, "creative_stream": { "innovative_approaches": [ "Hybrid model: B2B enterprise version of mobile app", "Freemium mobile with B2B premium features", "White-label mobile solution for B2B customers" ], "unconventional_solutions": "Use B2B customer base to beta test mobile app features", "recommendation": "Explore hybrid B2B-mobile convergence" }, "critical_stream": { "risk_assessment": { "pivot_risks": [ "revenue loss", "team expertise mismatch", "market timing" ], "status_quo_risks": [ "competitive displacement", "market saturation", "team motivation" ] }, "assumption_challenges": [ "Mobile app feedback may not translate to paying customers", "B2B growth stall might be temporary market condition" ], "recommendation": "Validate mobile monetization before full commitment" }, "synthetic_stream": { "integrated_perspective": "The decision isn't binary pivot vs. stay. The optimal path leverages existing B2B relationships to validate and fund mobile development.", "holistic_solution": "Phased approach: 1) Develop mobile features for B2B customers 2) Test monetization models 3) Gradually expand to B2C based on learnings", "confidence": 0.78 } }, "coordination_insights": [ "All streams agree on avoiding binary decision", "Creative and synthetic streams aligned on hybrid approach", "Critical stream's validation emphasis adopted by final recommendation" ], "final_synthesis": { "recommendation": "Pursue hybrid strategy with phased mobile development funded by B2B revenue", "rationale": "Minimizes risk while maximizing learning and optionality", "next_steps": [ "Survey B2B customers about mobile needs", "Develop mobile MVP for enterprise use", "Test B2C monetization models" ] } } ``` **When to Use:** - Complex strategic decisions - Need multiple perspectives - High-stakes choices - Creative problem-solving ## Problem Decomposition Tools ### decompose_problem - Complex Problem Breakdown Breaks complex challenges into manageable, prioritized components with dependency mapping. **Scenario**: You need to launch a new product in 6 months with limited resources. **Tool Call:** ```json { "tool": "decompose_problem", "arguments": { "input": "Launch new AI-powered project management tool in 6 months. Need to build MVP, acquire first 100 customers, establish support processes, and achieve $10k MRR. Team of 4 developers, $500k budget.", "max_depth": 3, "strategies": ["functional", "temporal", "resource"] } } ``` **Expected Response:** ```json { "problem_hierarchy": { "root_problem": "Launch AI project management tool (6 months, $10k MRR target)", "level_1_components": [ { "id": "product_development", "title": "Product Development", "description": "Build and test MVP with core AI features", "priority": "critical", "estimated_effort": "4 months", "dependencies": [] }, { "id": "market_validation", "title": "Market Validation & Customer Acquisition", "description": "Validate product-market fit and acquire first 100 customers", "priority": "critical", "estimated_effort": "6 months", "dependencies": ["product_development"] }, { "id": "business_operations", "title": "Business Operations Setup", "description": "Establish support, billing, and operational processes", "priority": "high", "estimated_effort": "2 months", "dependencies": ["product_development"] } ], "level_2_components": { "product_development": [ { "id": "ai_engine", "title": "AI Engine Development", "description": "Core AI algorithms for project prediction and optimization", "priority": "critical", "estimated_effort": "3 months", "resource_requirements": ["2 ML engineers", "GPU infrastructure"] }, { "id": "user_interface", "title": "User Interface", "description": "Intuitive dashboard and mobile-responsive design", "priority": "high", "estimated_effort": "2 months", "resource_requirements": ["1 frontend developer", "1 UX designer"] }, { "id": "integration_apis", "title": "Integration APIs", "description": "Connect with popular tools (Slack, Jira, GitHub)", "priority": "medium", "estimated_effort": "1.5 months", "resource_requirements": ["1 backend developer"] } ] } }, "dependency_analysis": { "critical_path": [ "ai_engine → user_interface → beta_testing → customer_acquisition → revenue_generation" ], "bottlenecks": [ { "component": "ai_engine", "reason": "Blocks all other development and testing", "mitigation": "Start with simpler ML models, iterate complexity" } ], "parallel_opportunities": [ "UI development can start with mockups while AI engine is in progress", "Market research and customer interviews can run parallel to development" ] }, "resource_allocation": { "team_assignments": { "ai_engine": ["developer_1", "developer_2"], "user_interface": ["developer_3"], "integration_apis": ["developer_4"], "market_validation": ["founder", "part_time_marketer"] }, "budget_allocation": { "development": "$300k (60%)", "infrastructure": "$100k (20%)", "marketing": "$75k (15%)", "operations": "$25k (5%)" } }, "risk_assessment": { "high_risk_components": [ { "component": "ai_engine", "risks": ["technical complexity", "performance requirements"], "mitigation": "Prototype early, validate with simple models first" } ], "timeline_risks": [ { "risk": "AI development delays", "probability": 0.6, "impact": "2-month delay", "mitigation": "Parallel development of non-AI features" } ] } } ``` **When to Use:** - Large, complex projects - Resource planning needed - Multiple dependencies - Team coordination required ## Probabilistic Reasoning Tools ### think_probabilistic - Uncertainty and Evidence Integration Processes problems using Bayesian reasoning with uncertainty quantification. **Scenario**: You're evaluating whether to invest in a new technology for your company. **Tool Call:** ```json { "tool": "think_probabilistic", "arguments": { "input": "Should we adopt blockchain technology for our supply chain? Initial research suggests 40% cost reduction potential, but implementation would cost $2M and take 18 months. Some competitors are already using it successfully.", "enable_bayesian_updating": true, "max_hypotheses": 3, "uncertainty_threshold": 0.1 } } ``` **Expected Response:** ```json { "hypotheses": [ { "hypothesis": "Blockchain adoption will provide significant competitive advantage", "prior_probability": 0.3, "evidence_integration": [ { "evidence": "Competitors showing success", "likelihood": 0.7, "weight": 0.8 }, { "evidence": "40% cost reduction potential", "likelihood": 0.6, "weight": 0.9 }, { "evidence": "High implementation cost and time", "likelihood": 0.9, "weight": 0.7 } ], "posterior_probability": 0.52, "confidence_interval": [0.38, 0.66] }, { "hypothesis": "Blockchain adoption will provide marginal benefits", "prior_probability": 0.5, "posterior_probability": 0.35, "confidence_interval": [0.22, 0.48] }, { "hypothesis": "Blockchain adoption will be net negative", "prior_probability": 0.2, "posterior_probability": 0.13, "confidence_interval": [0.05, 0.25] } ], "uncertainty_analysis": { "key_uncertainties": [ { "factor": "Actual cost reduction achievable", "uncertainty_level": 0.4, "impact_on_decision": "high" }, { "factor": "Technology maturity and stability", "uncertainty_level": 0.3, "impact_on_decision": "medium" }, { "factor": "Competitive response timing", "uncertainty_level": 0.6, "impact_on_decision": "medium" } ], "information_value": [ { "information": "Pilot project results from similar company", "value_of_information": 0.8, "cost_to_obtain": "low" }, { "information": "Detailed technical feasibility study", "value_of_information": 0.7, "cost_to_obtain": "medium" } ] }, "decision_recommendation": { "recommended_action": "Conduct limited pilot project before full commitment", "reasoning": "52% probability of significant advantage justifies exploration, but high uncertainty suggests staged approach", "risk_mitigation": "Pilot reduces uncertainty while limiting downside exposure", "decision_confidence": 0.73 }, "bayesian_updates": { "if_pilot_succeeds": { "updated_probability": 0.78, "recommendation": "Proceed with full implementation" }, "if_pilot_fails": { "updated_probability": 0.15, "recommendation": "Abandon blockchain approach" } } } ``` **When to Use:** - High-uncertainty decisions - Multiple competing hypotheses - Need for evidence integration - Risk assessment scenarios ## Memory Management Tools ### analyze_memory_usage - Memory System Analysis Analyzes memory usage patterns and identifies optimization opportunities. **Tool Call:** ```json { "tool": "analyze_memory_usage", "arguments": { "analysis_depth": "deep", "include_recommendations": true } } ``` **Expected Response:** ```json { "memory_overview": { "total_memories": 2847, "episodic_memories": 1203, "semantic_memories": 1644, "memory_health_score": 0.73, "storage_efficiency": 0.68 }, "usage_patterns": { "most_accessed_domains": [ { "domain": "software-development", "access_count": 342 }, { "domain": "project-management", "access_count": 198 }, { "domain": "personal-productivity", "access_count": 156 } ], "least_accessed_memories": { "count": 847, "percentage": 29.8, "oldest_unused": "2023-08-15" }, "memory_age_distribution": { "recent_7_days": 156, "last_month": 423, "last_quarter": 892, "older": 1376 } }, "optimization_opportunities": [ { "type": "selective_forgetting", "description": "Archive 847 memories unused for 6+ months", "potential_benefit": "Improve retrieval speed by 23%", "risk_level": "low" }, { "type": "memory_consolidation", "description": "Merge 156 similar semantic concepts", "potential_benefit": "Reduce storage by 12%, improve coherence", "risk_level": "medium" } ], "recommendations": [ { "priority": "high", "action": "Enable automatic archiving of unused memories", "expected_impact": "Maintain optimal performance as memory grows" }, { "priority": "medium", "action": "Increase consolidation frequency for active domains", "expected_impact": "Better knowledge integration and retrieval" } ] } ``` ### optimize_memory - Memory Optimization Executes memory optimization through selective forgetting and consolidation. **Tool Call:** ```json { "tool": "optimize_memory", "arguments": { "optimization_mode": "moderate", "target_memory_reduction": 0.15, "preserve_important_memories": true, "require_user_consent": true } } ``` **Expected Response:** ```json { "optimization_plan": { "memories_to_archive": 427, "memories_to_consolidate": 89, "memories_to_delete": 12, "estimated_performance_gain": "18% faster retrieval", "estimated_storage_reduction": "15.2%" }, "user_consent_required": [ { "memory_id": "mem_12847", "content": "Meeting notes from Q2 planning session...", "reason": "High importance but old, confirm deletion", "recommendation": "archive" } ], "automatic_optimizations": [ { "type": "consolidation", "description": "Merged 23 similar React development concepts", "impact": "Improved semantic coherence" } ] } ``` ### recover_memory - Memory Recovery Attempts to recover degraded or forgotten memories using associative cues. **Tool Call:** ```json { "tool": "recover_memory", "arguments": { "memory_id": "mem_15234", "recovery_cues": [ { "type": "semantic", "value": "database optimization performance", "strength": 0.8 }, { "type": "temporal", "value": "2024-03-15", "strength": 0.6 }, { "type": "emotional", "value": "frustration breakthrough", "strength": 0.7 } ], "max_recovery_attempts": 5 } } ``` **Expected Response:** ```json { "recovery_result": { "success": true, "confidence": 0.73, "recovered_content": "Discovered that adding composite index on (user_id, created_at) reduced query time from 2.3s to 0.12s. The breakthrough came after realizing the ORDER BY clause was causing full table scans.", "recovery_method": "associative_reconstruction", "supporting_memories": [ "Database indexing best practices", "Query optimization techniques", "Performance debugging session notes" ] }, "recovery_quality": { "completeness": 0.85, "accuracy_estimate": 0.78, "missing_elements": ["specific table names", "exact query syntax"] } } ``` ## Advanced Usage Patterns ### Combining Tools for Complex Analysis **Scenario**: Strategic planning for product roadmap ```json // Step 1: Decompose the planning challenge { "tool": "decompose_problem", "arguments": { "input": "Plan product roadmap for next 18 months with 3 major features, limited engineering resources, and uncertain market conditions" } } // Step 2: Analyze each component systematically { "tool": "analyze_systematically", "arguments": { "input": "Prioritize features based on customer value, technical complexity, and market timing" } } // Step 3: Use parallel reasoning for strategic decisions { "tool": "think_parallel", "arguments": { "input": "Should we focus on enterprise features or consumer features first?" } } // Step 4: Apply probabilistic reasoning to uncertain outcomes { "tool": "think_probabilistic", "arguments": { "input": "What's the probability each roadmap option will achieve our revenue targets?" } } ``` ### Memory-Enhanced Problem Solving ```json // Step 1: Recall relevant experience { "tool": "recall", "arguments": { "cue": "product roadmap planning challenges" } } // Step 2: Analyze with memory context { "tool": "think_parallel", "arguments": { "input": "Current roadmap decision with context from past planning experiences" } } // Step 3: Store insights for future use { "tool": "remember", "arguments": { "content": "Key lessons from roadmap planning process and outcomes" } } ``` ## Key Takeaways ### When to Use Advanced Tools - **analyze_systematically**: Unfamiliar problems needing structure - **think_parallel**: Complex decisions requiring multiple perspectives - **decompose_problem**: Large projects with many dependencies - **think_probabilistic**: High-uncertainty situations with evidence - **Memory tools**: Long-term learning and optimization ### Tool Combinations - **Systematic + Parallel**: Structure then explore perspectives - **Decomposition + Probabilistic**: Break down then assess uncertainties - **Memory + Any tool**: Leverage past experience for better decisions ### Best Practices 1. **Start simple**: Use basic tools before advanced ones 2. **Combine strategically**: Each tool adds specific value 3. **Consider context**: Match tool to problem characteristics 4. **Learn iteratively**: Build expertise through practice 5. **Optimize memory**: Regular maintenance improves performance ## Next Steps - **[Complete Workflow](complete-workflow.md)** - See all tools working together - **[Real-World Examples](../real-world/)** - Complex applications - **[Integration Examples](../integration/)** - Implementation patterns --- _Ready for comprehensive workflows? Check out [Complete Workflow](complete-workflow.md) to see all tools working together._