Design Patterns MCP Server

{ "patterns": [ { "name": "Prompt Chaining", "id": "prompt-chaining", "category": "AI/ML", "description": "Breaks down complex problems into a sequence of smaller, manageable sub-problems, each addressed through specifically designed prompts where the output of one becomes the input for the next.", "when_to_use": [ "Tasks too complex for a single prompt", "Sequential processing needed", "Information processing workflows", "Complex query answering" ], "benefits": [ "Improved reliability and manageability", "Modularity and clarity", "Enhanced accuracy", "Integration of external tools" ], "drawbacks": [ "Increased complexity", "Potential for error propagation if not structured properly" ], "use_cases": [ "Automated content analysis", "AI-driven research assistants", "Complex report generation", "Multi-step inference" ], "complexity": "Medium", "tags": [ "prompt-chaining", "pipeline", "sequential", "modularity", "agentic", "ai-ml" ] }, { "name": "Routing Pattern", "id": "routing-pattern", "category": "AI/ML", "description": "Introduces conditional logic into an agent's operational framework, enabling dynamic evaluation of criteria to select from a set of possible subsequent actions, moving beyond fixed execution paths.", "when_to_use": [ "Adaptive responses required", "Classifying and directing queries", "Multiple potential actions based on input", "Dynamic decision-making" ], "benefits": [ "Flexibility", "Context-aware behavior", "Dynamic decision-making", "Better handling of diverse inputs" ], "drawbacks": [ "Implementation complexity", "Potential for misrouting if criteria are not well-defined" ], "use_cases": [ "Virtual assistants", "AI-driven tutors", "Automated data processing pipelines", "Multi-agent systems" ], "complexity": "Medium", "tags": [ "routing", "conditional-logic", "delegation", "dynamic", "agentic", "ai-ml" ] }, { "name": "Parallelization Pattern", "id": "parallelization-pattern", "category": "AI/ML", "description": "Executes multiple components, such as LLM calls, tool usages, or sub-agents, concurrently to reduce overall execution time for tasks with independent parts.", "when_to_use": [ "Tasks can be broken into independent sub-tasks", "External services with latency", "Concurrent processing possible" ], "benefits": [ "Reduced execution time", "Improved efficiency", "Better resource utilization" ], "drawbacks": [ "Coordination complexity", "Potential race conditions", "Requires asynchronous support" ], "use_cases": [ "Research agents", "Data processing pipelines", "Multi-source information gathering" ], "complexity": "Medium", "tags": [ "parallelization", "concurrency", "efficiency", "asynchronous", "agentic", "ai-ml" ] }, { "name": "Planning Pattern", "id": "planning-pattern", "category": "AI/ML", "description": "Enables an agent to autonomously chart a course to a goal by understanding initial and goal states, discovering optimal sequences of actions, and adapting to new information.", "when_to_use": [ "Complex goals where how is not predetermined", "Dynamic environments", "Need for adaptability" ], "benefits": [ "Goal-oriented behavior", "Adaptability", "Structured problem-solving" ], "drawbacks": [ "Trade-off with predictability", "Potential for unpredictable behavior", "Requires careful constraint setting" ], "use_cases": [ "Procedural task automation", "Autonomous navigation", "Information synthesis", "Customer support" ], "complexity": "High", "tags": [ "planning", "goal-oriented", "adaptability", "sequence", "agentic", "ai-ml" ] }, { "name": "Memory Management Pattern", "id": "memory-management-pattern", "category": "AI/ML", "description": "Manages state and memory across interactions, allowing agents to retain information, maintain context, and learn from past experiences.", "when_to_use": [ "Conversational continuity needed", "State persistence required", "Learning from interactions" ], "benefits": [ "Context retention", "Improved user experience", "Knowledge accumulation" ], "drawbacks": [ "Storage requirements", "Privacy concerns", "Complexity in implementation" ], "use_cases": [ "Chatbots", "Personal assistants", "Long-term agent interactions" ], "complexity": "Medium", "tags": [ "memory", "state", "persistence", "context", "agentic", "ai-ml" ] }, { "name": "Learning and Adaptation Pattern", "id": "learning-adaptation-pattern", "category": "AI/ML", "description": "Allows agents to learn from interactions, adapt behavior, and improve performance over time through feedback and experience.", "when_to_use": [ "Continuous improvement needed", "Dynamic tasks", "Personalization" ], "benefits": [ "Self-improvement", "Adaptation to new scenarios", "Personalized responses" ], "drawbacks": [ "Requires feedback mechanisms", "Potential overfitting", "Resource intensive" ], "use_cases": [ "Personalization systems", "Adaptive assistants", "Evolving workflows" ], "complexity": "High", "tags": [ "learning", "adaptation", "feedback", "improvement", "agentic", "ai-ml" ] }, { "name": "Model Context Protocol (MCP)", "id": "model-context-protocol", "category": "AI/ML", "description": "Defines a protocol for managing and sharing context between models and systems, enabling better integration and communication.", "when_to_use": [ "Interoperability between models", "Context sharing needed", "Integrated AI systems" ], "benefits": [ "Standardized communication", "Better integration", "Enhanced context handling" ], "drawbacks": [ "Protocol complexity", "Compatibility issues", "Overhead" ], "use_cases": [ "Multi-model systems", "Context-aware applications", "Integrated AI platforms" ], "complexity": "Medium", "tags": [ "protocol", "context", "integration", "communication", "agentic", "ai-ml" ] }, { "name": "Goal Setting and Monitoring Pattern", "id": "goal-setting-monitoring-pattern", "category": "AI/ML", "description": "Involves setting clear, measurable goals and continuously monitoring progress towards them, allowing agents to track and adjust their actions.", "when_to_use": [ "Long-term tasks", "Progress tracking needed", "Adjustable objectives" ], "benefits": [ "Focused execution", "Progress visibility", "Adaptive adjustments" ], "drawbacks": [ "Goal definition complexity", "Monitoring overhead", "Potential misalignment" ], "use_cases": [ "Project management", "Performance tracking", "Goal-oriented agents" ], "complexity": "Medium", "tags": [ "goals", "monitoring", "tracking", "adjustment", "agentic", "ai-ml" ] }, { "name": "Exception Handling and Recovery Pattern", "id": "exception-handling-recovery-pattern", "category": "AI/ML", "description": "Provides mechanisms for detecting, handling, and recovering from errors or unexpected situations in agent operations.", "when_to_use": [ "Unreliable environments", "Error-prone tasks", "Robustness required" ], "benefits": [ "Improved reliability", "Graceful failure handling", "System stability" ], "drawbacks": [ "Complexity in error detection", "Recovery strategy design", "Potential performance impact" ], "use_cases": [ "Fault-tolerant systems", "Error-prone workflows", "Critical applications" ], "complexity": "Medium", "tags": [ "exception", "recovery", "error-handling", "robustness", "agentic", "ai-ml" ] }, { "name": "Human-in-the-Loop Pattern", "id": "human-in-the-loop-pattern", "category": "AI/ML", "description": "Incorporates human oversight, intervention, or feedback into the agent's decision-making process to improve accuracy and trust.", "when_to_use": [ "High-stakes decisions", "Complex judgments", "Trust and accuracy critical" ], "benefits": [ "Improved accuracy", "Human oversight", "Better handling of edge cases" ], "drawbacks": [ "Latency from human involvement", "Scalability issues", "Dependency on humans" ], "use_cases": [ "Medical diagnosis", "Legal analysis", "Quality control" ], "complexity": "Medium", "tags": [ "human-in-loop", "oversight", "feedback", "trust", "agentic", "ai-ml" ] }, { "name": "Inter-Agent Communication (A2A) Pattern", "id": "inter-agent-communication-pattern", "category": "AI/ML", "description": "Defines protocols and mechanisms for communication and coordination between multiple agents in a system.", "when_to_use": [ "Multi-agent systems", "Coordination needed", "Distributed tasks" ], "benefits": [ "Better collaboration", "Scalability", "Specialized task handling" ], "drawbacks": [ "Communication overhead", "Synchronization challenges", "Complexity" ], "use_cases": [ "Distributed AI systems", "Collaborative workflows", "Agent networks" ], "complexity": "High", "tags": [ "inter-agent", "communication", "coordination", "collaboration", "agentic", "ai-ml" ] }, { "name": "Resource-Aware Optimization Pattern", "id": "resource-aware-optimization-pattern", "category": "AI/ML", "description": "Optimizes agent operations considering resource constraints like compute, memory, and time, ensuring efficient use of available resources.", "when_to_use": [ "Resource-limited environments", "Cost optimization", "Performance tuning" ], "benefits": [ "Efficient resource use", "Cost reduction", "Better performance" ], "drawbacks": [ "Optimization complexity", "Trade-off decisions", "Monitoring requirements" ], "use_cases": [ "Edge computing", "Cloud deployments", "Resource-constrained systems" ], "complexity": "High", "tags": [ "resource", "optimization", "efficiency", "constraints", "agentic", "ai-ml" ] }, { "name": "Reasoning Techniques Pattern", "id": "reasoning-techniques-pattern", "category": "AI/ML", "description": "Applies various reasoning methods like chain-of-thought, tree-of-thought, or other logical processes to enhance decision-making and problem-solving.", "when_to_use": [ "Complex reasoning required", "Multi-step analysis", "Logical problem-solving" ], "benefits": [ "Improved reasoning", "Better decisions", "Transparency" ], "drawbacks": [ "Increased computation", "Complexity", "Potential verbosity" ], "use_cases": [ "Mathematical problems", "Strategic planning", "Analysis tasks" ], "complexity": "High", "tags": [ "reasoning", "logic", "decision-making", "analysis", "agentic", "ai-ml" ] }, { "name": "Guardrails/Safety Patterns", "id": "guardrails-safety-patterns", "category": "AI/ML", "description": "Implements safety measures, constraints, and guardrails to prevent harmful outputs, ensure ethical behavior, and maintain system security.", "when_to_use": [ "Safety-critical applications", "Ethical concerns", "Harm prevention" ], "benefits": [ "Enhanced safety", "Ethical compliance", "Risk reduction" ], "drawbacks": [ "Implementation overhead", "Potential creativity limits", "False positives" ], "use_cases": [ "Content moderation", "Autonomous systems", "Public-facing AI" ], "complexity": "Medium", "tags": [ "safety", "guardrails", "ethics", "security", "agentic", "ai-ml" ] }, { "name": "Evaluation and Monitoring Pattern", "id": "evaluation-monitoring-pattern", "category": "AI/ML", "description": "Continuously evaluates agent performance, monitors behavior, and collects metrics to ensure quality and enable improvements.", "when_to_use": [ "Performance tracking", "Quality assurance", "Continuous improvement" ], "benefits": [ "Quality assurance", "Performance insights", "Iterative improvement" ], "drawbacks": [ "Monitoring overhead", "Metric design complexity", "Data collection challenges" ], "use_cases": [ "Production systems", "Research platforms", "Quality control" ], "complexity": "Medium", "tags": [ "evaluation", "monitoring", "metrics", "quality", "agentic", "ai-ml" ] }, { "name": "Prioritization Pattern", "id": "prioritization-pattern", "category": "AI/ML", "description": "Enables agents to prioritize tasks, requests, or actions based on importance, urgency, or other criteria to optimize resource allocation.", "when_to_use": [ "Multiple concurrent tasks", "Resource constraints", "Time-sensitive operations" ], "benefits": [ "Optimized resource use", "Better task management", "Improved efficiency" ], "drawbacks": [ "Prioritization logic complexity", "Potential bias", "Dynamic adjustment needs" ], "use_cases": [ "Task schedulers", "Resource managers", "Workflow systems" ], "complexity": "Medium", "tags": [ "prioritization", "scheduling", "optimization", "resources", "agentic", "ai-ml" ] }, { "name": "Exploration and Discovery Pattern", "id": "exploration-discovery-pattern", "category": "AI/ML", "description": "Allows agents to explore new possibilities, discover patterns, or generate novel solutions through curiosity-driven or systematic exploration.", "when_to_use": [ "Innovation needed", "Unknown domains", "Creative problem-solving" ], "benefits": [ "Novel solutions", "Adaptation to new scenarios", "Knowledge expansion" ], "drawbacks": [ "Unpredictability", "Resource consumption", "Evaluation challenges" ], "use_cases": [ "Research agents", "Creative systems", "Adaptive learning" ], "complexity": "High", "tags": [ "exploration", "discovery", "innovation", "creativity", "agentic", "ai-ml" ] } ] }