Design Patterns MCP Server

{ "patterns": [ { "id": "rag-pattern", "name": "Retrieval-Augmented Generation (RAG)", "category": "AI/ML", "description": "Combines retrieval with generation for improved responses using external knowledge", "when_to_use": ["Need accurate factual responses", "Reduce hallucinations", "Domain-specific knowledge"], "benefits": ["Improved accuracy", "Factual responses", "Up-to-date information", "Reduced hallucinations"], "drawbacks": ["Complex implementation", "Vector database required", "Latency overhead"], "use_cases": ["Chatbots", "Q&A systems", "Content generation", "Documentation assistance"], "complexity": "High", "tags": ["rag", "retrieval", "generation", "llm"] }, { "id": "reflection-pattern", "name": "Reflection Pattern", "category": "AI/ML", "description": "AI agent reflects on its own outputs to improve reasoning and catch errors", "when_to_use": ["Complex reasoning tasks", "Error detection needed", "Multi-step problems"], "benefits": ["Self-correction", "Improved accuracy", "Better reasoning", "Error detection"], "drawbacks": ["Increased compute cost", "Higher latency", "Complexity"], "use_cases": ["Code generation", "Mathematical reasoning", "Complex analysis"], "complexity": "High", "tags": ["reflection", "self-correction", "reasoning"] }, { "id": "tool-use-pattern", "name": "Tool Use Pattern", "category": "AI/ML", "description": "Enables AI agents to use external tools and APIs to extend capabilities", "when_to_use": ["Need external data", "Calculations required", "API integrations"], "benefits": ["Extended capabilities", "Real-time data", "Specialized functions", "Accurate computations"], "drawbacks": ["Tool reliability dependency", "Security concerns", "Complexity"], "use_cases": ["Code execution", "API calls", "Data analysis", "Web search"], "complexity": "Medium", "tags": ["tools", "apis", "integration"] }, { "id": "multi-agent-pattern", "name": "Multi-Agent Pattern", "category": "AI/ML", "description": "Multiple specialized agents collaborate to solve complex problems", "when_to_use": ["Complex workflows", "Specialized expertise needed", "Parallel processing"], "benefits": ["Specialization", "Scalability", "Parallel processing", "Expertise distribution"], "drawbacks": ["Coordination complexity", "Communication overhead", "Consistency challenges"], "use_cases": ["Software development", "Research analysis", "Content creation"], "complexity": "Very High", "tags": ["multi-agent", "collaboration", "workflow"] }, { "id": "chain-of-thought", "name": "Chain-of-Thought Prompting", "category": "AI/ML", "description": "Breaks down complex problems into step-by-step reasoning chains", "when_to_use": ["Complex reasoning", "Mathematical problems", "Multi-step analysis"], "benefits": ["Improved reasoning", "Transparency", "Better accuracy", "Debuggable"], "drawbacks": ["Longer responses", "Higher token usage", "Potential verbosity"], "use_cases": ["Math problems", "Logic puzzles", "Analysis tasks"], "complexity": "Low", "tags": ["reasoning", "step-by-step", "prompting"] }, { "id": "few-shot-learning", "name": "Few-Shot Learning", "category": "AI/ML", "description": "Provides examples in prompts to guide AI behavior and output format", "when_to_use": ["Specific output format", "Consistent behavior", "Task demonstration"], "benefits": ["Consistent outputs", "Quick adaptation", "No fine-tuning needed", "Format control"], "drawbacks": ["Context length limits", "Example selection critical", "Token usage"], "use_cases": ["Data formatting", "Classification", "Style matching"], "complexity": "Low", "tags": ["few-shot", "examples", "prompting"] }, { "id": "tree-of-thought", "name": "Tree-of-Thought", "category": "AI/ML", "description": "Explores multiple reasoning paths simultaneously like a decision tree", "when_to_use": ["Complex decision making", "Multiple solution paths", "Strategic planning"], "benefits": ["Comprehensive exploration", "Better decisions", "Multiple perspectives", "Backtracking ability"], "drawbacks": ["High computational cost", "Complex implementation", "Exponential growth"], "use_cases": ["Game playing", "Strategic planning", "Complex problem solving"], "complexity": "Very High", "tags": ["tree-search", "reasoning", "decision-making"] }, { "id": "agent-loop", "name": "Agent Loop Pattern", "category": "AI/ML", "description": "Continuous cycle of observation, reasoning, action, and feedback", "when_to_use": ["Interactive environments", "Continuous learning", "Adaptive behavior"], "benefits": ["Continuous improvement", "Adaptive behavior", "Real-time response", "Learning capability"], "drawbacks": ["Resource intensive", "Potential loops", "Complexity"], "use_cases": ["Autonomous agents", "Game AI", "Robotic control"], "complexity": "High", "tags": ["agent", "loop", "continuous"] }, { "id": "embeddings-pattern", "name": "Embeddings Pattern", "category": "AI/ML", "description": "Converts text/data into high-dimensional vectors for similarity and search", "when_to_use": ["Semantic search", "Similarity matching", "Clustering"], "benefits": ["Semantic understanding", "Efficient search", "Similarity computation", "Clustering capability"], "drawbacks": ["Storage requirements", "Dimensionality choice", "Quality dependence"], "use_cases": ["Search engines", "Recommendation systems", "Document clustering"], "complexity": "Medium", "tags": ["embeddings", "vectors", "similarity"] }, { "id": "fine-tuning-pattern", "name": "Fine-Tuning Pattern", "category": "AI/ML", "description": "Adapts pre-trained models to specific domains or tasks", "when_to_use": ["Domain-specific tasks", "Improve performance", "Custom behavior"], "benefits": ["Better performance", "Domain adaptation", "Custom behavior", "Efficiency"], "drawbacks": ["Requires training data", "Computational cost", "Overfitting risk"], "use_cases": ["Domain chatbots", "Classification", "Text generation"], "complexity": "High", "tags": ["fine-tuning", "training", "adaptation"] }, { "id": "prompt-engineering", "name": "Prompt Engineering", "category": "AI/ML", "description": "Systematic design of prompts to elicit desired AI behavior and outputs", "when_to_use": ["Optimize AI responses", "Control output format", "Improve accuracy"], "benefits": ["Better results", "Consistent behavior", "No training needed", "Quick iteration"], "drawbacks": ["Trial and error", "Model dependency", "Prompt brittleness"], "use_cases": ["Content generation", "Data extraction", "Classification"], "complexity": "Low", "tags": ["prompting", "optimization", "engineering"] }, { "id": "model-ensemble", "name": "Model Ensemble", "category": "AI/ML", "description": "Combines multiple models to improve performance and reliability", "when_to_use": ["Improve accuracy", "Reduce overfitting", "Increase robustness"], "benefits": ["Better accuracy", "Reduced variance", "Robustness", "Error reduction"], "drawbacks": ["Increased complexity", "Higher cost", "Slower inference"], "use_cases": ["Predictions", "Classification", "Recommendation"], "complexity": "High", "tags": ["ensemble", "combination", "voting"] }, { "id": "active-learning", "name": "Active Learning", "category": "AI/ML", "description": "Selectively queries for labels on most informative examples", "when_to_use": ["Limited labeled data", "Expensive labeling", "Continuous learning"], "benefits": ["Efficient labeling", "Improved performance", "Cost reduction", "Targeted learning"], "drawbacks": ["Complex implementation", "Query strategy critical", "Cold start problem"], "use_cases": ["Data annotation", "Model improvement", "Human-in-loop"], "complexity": "High", "tags": ["active-learning", "labeling", "optimization"] }, { "id": "transfer-learning", "name": "Transfer Learning", "category": "AI/ML", "description": "Leverages knowledge from pre-trained models for new tasks", "when_to_use": ["Limited training data", "Similar domains", "Faster training"], "benefits": ["Faster training", "Better performance", "Less data needed", "Knowledge reuse"], "drawbacks": ["Domain similarity required", "Potential negative transfer", "Model selection"], "use_cases": ["Image classification", "NLP tasks", "Domain adaptation"], "complexity": "Medium", "tags": ["transfer", "pre-trained", "adaptation"] }, { "id": "federated-learning", "name": "Federated Learning", "category": "AI/ML", "description": "Trains models across distributed devices without centralizing data", "when_to_use": ["Privacy requirements", "Distributed data", "Edge computing"], "benefits": ["Privacy preservation", "Distributed training", "Reduced data transfer", "Edge deployment"], "drawbacks": ["Communication overhead", "Heterogeneity challenges", "Coordination complexity"], "use_cases": ["Mobile AI", "Healthcare", "IoT systems"], "complexity": "Very High", "tags": ["federated", "distributed", "privacy"] }, { "id": "continual-learning", "name": "Continual Learning", "category": "AI/ML", "description": "Learns new tasks while retaining knowledge of previous tasks", "when_to_use": ["Sequential tasks", "Lifelong learning", "Dynamic environments"], "benefits": ["Knowledge retention", "Continuous adaptation", "No catastrophic forgetting", "Efficiency"], "drawbacks": ["Complex algorithms", "Memory requirements", "Stability-plasticity dilemma"], "use_cases": ["Personalization", "Adaptive systems", "Lifelong AI"], "complexity": "Very High", "tags": ["continual", "lifelong", "adaptation"] }, { "id": "meta-learning", "name": "Meta-Learning", "category": "AI/ML", "description": "Learns how to learn, enabling fast adaptation to new tasks", "when_to_use": ["Few examples available", "Fast adaptation needed", "Multiple related tasks"], "benefits": ["Fast adaptation", "Sample efficiency", "Generalization", "Task diversity"], "drawbacks": ["Complex implementation", "Meta-training required", "Task distribution dependency"], "use_cases": ["Few-shot learning", "Personalization", "Rapid adaptation"], "complexity": "Very High", "tags": ["meta-learning", "adaptation", "few-shot"] }, { "id": "self-supervised-learning", "name": "Self-Supervised Learning", "category": "AI/ML", "description": "Creates supervision signals from the data itself without manual labels", "when_to_use": ["Limited labeled data", "Large unlabeled datasets", "Representation learning"], "benefits": ["No manual labeling", "Scalable", "Rich representations", "Domain adaptation"], "drawbacks": ["Task design critical", "Evaluation challenges", "Proxy task quality"], "use_cases": ["Representation learning", "Pre-training", "Data-rich domains"], "complexity": "High", "tags": ["self-supervised", "representation", "unlabeled"] }, { "id": "attention-mechanism", "name": "Attention Mechanism", "category": "AI/ML", "description": "Focuses on relevant parts of input when making predictions", "when_to_use": ["Sequence modeling", "Variable input lengths", "Relevance weighting"], "benefits": ["Improved performance", "Interpretability", "Variable lengths", "Parallelization"], "drawbacks": ["Computational overhead", "Memory requirements", "Attention collapse"], "use_cases": ["Machine translation", "Text summarization", "Image captioning"], "complexity": "High", "tags": ["attention", "focus", "weighting"] }, { "id": "transformer-pattern", "name": "Transformer Pattern", "category": "AI/ML", "description": "Architecture based entirely on attention mechanisms for sequence processing", "when_to_use": ["Language modeling", "Sequence-to-sequence tasks", "Large-scale AI"], "benefits": ["Parallelizable", "Long-range dependencies", "Transfer learning", "Scalability"], "drawbacks": ["Computational requirements", "Memory intensive", "Training complexity"], "use_cases": ["Language models", "Machine translation", "Text generation"], "complexity": "Very High", "tags": ["transformer", "attention", "sequence"] } ] }