Thinking Patterns MCP Server
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@followed by the MCP server name and your instructions, e.g., "@Thinking Patterns MCP ServerUse sequential thinking to analyze the root cause of the server outage."
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Here is a step-by-step guide with screenshots.
Thinking Patterns MCP Server
TL;DR

A comprehensive MCP server that provides AI systems with structured thinking frameworks that follow existing problem-solving paradigms. Transform abstract cognitive patterns into concrete, invocable tools. Enforces adherence to the paradigms through schema validation.
📚 Documentation
Quick Start - Get up and running in 2 minutes
TOOL_REFERENCE.md - Complete tool specifications and parameters
EXAMPLES.md - Detailed usage examples and patterns
SUMMARY.md - Executive summary and overview
SYSTEM_INTENT.md - System purpose and philosophy
TECHNICAL_ARCHITECTURE.md - Technical implementation details
Problem
LLMs struggle to maintain consistent reasoning patterns throughout extended conversations due to context window limitations and degrading adherence to initial instructions. Traditional approaches like "keep X in mind" or role-based prompting fail when:
Critical context passes out of the attention window
Models acknowledge constraints but don't consistently apply them
Reasoning structures decay over multi-turn interactions
thinking-patterns enforces structural consistency through interactive schema validation rather than passive instruction-following.
Solution
thinking-patterns works by implicitly encouraging good engineering behaviors and approaches:
Schema validation ensures reasoning patterns persist beyond context window limits
Structural requirements live in tool definitions rather than repeated prompt text, reducing token overhead
Tool interaction success/failure provides objective metrics for reasoning quality
Indicates to the model that they are not following the pattern appropriately without additional user interaction
Reusable thinking structures across different problem domains
Reasoning Improvements
Attention Drift Prevention: Without structural anchors, models experience "goal drift" as new contextual information competes with original objectives. Schema validation creates persistent attention anchors.
State Crystallization: Explicit articulation of reasoning state (forced by tool parameters) appears to strengthen internal representation compared to implicit state maintenance. Models demonstrate measurably better state consistency when reasoning is externalized.
Error-Driven Learning: Schema validation errors create immediate, interactive corrective feedback loops within a single response, unlike instruction-based approaches where non-compliance often goes undetected until task completion.
Cognitive Load Distribution: Externalizing structural requirements to schemas allows models to allocate more processing capacity to problem-solving rather than format compliance, similar to how humans benefit from external memory aids.
Iterative Reinforcement: Repeated successful tool interactions strengthen adherence patterns through practice, creating compound consistency benefits over conversation length.
🚀 Quick Start
1. Install (Choose One)
Recommended: Smithery (for Cursor users)
npx -y @smithery/cli install @emmahyde/thinking-patterns --client cursorNPM
npm install @emmahyde/thinking-patternsNPX (no installation)
npx -y @emmahyde/thinking-patterns2. Configure MCP Client
Add to your MCP client configuration:
{
"mcpServers": {
"thinking-patterns": {
"command": "npx",
"args": ["-y", "@emmahyde/thinking-patterns"]
}
}
}3. Try Your First Tool
# Example: Use sequential thinking for planning
{
"tool": "sequential_thinking",
"arguments": {
"thought": "Plan a product launch strategy",
"thoughtNumber": 1,
"totalThoughts": 5
}
}🧠 Available Thinking Tools
Core Systematic Thinking
sequential_thinking - Multi-step reasoning with revision support
problem_decomposition - Break complex problems into manageable parts
recursive_thinking - Apply recursive strategies to self-similar problems
Mental Models & Frameworks
mental_model - Apply proven frameworks (First Principles, Inversion, etc.)
decision_framework - Multi-criteria decision analysis
domain_modeling - Create conceptual models of problem domains
Scientific & Critical Analysis
scientific_method - Formal hypothesis testing and experimentation
critical_thinking - Systematic evaluation of arguments and assumptions
debugging_approach - Systematic troubleshooting methodologies
Collaborative & Dialectical
collaborative_reasoning - Multi-perspective problem solving with personas
structured_argumentation - Dialectical reasoning and argument analysis
Advanced Cognitive Patterns
metacognitive_monitoring - Self-assessment of reasoning quality
visual_reasoning - Diagram-based thinking and spatial reasoning
temporal_thinking - Time-based system analysis with state transitions
Probabilistic & Optimization
stochastic_algorithm - Decision-making under uncertainty [BETA]
Markov Decision Processes (MDPs)
Monte Carlo Tree Search (MCTS)
Multi-Armed Bandit algorithms
Bayesian Optimization
Hidden Markov Models (HMMs)
💡 Recommended Starting Points
sequential_thinking & problem_decomposition - Perfect for planning and breaking down complex tasks
debugging_approach - Send error messages directly for systematic troubleshooting
collaborative_reasoning - Simulate team discussions to uncover blind spots
📋 Prerequisites
Node.js 18+ (for local installation)
MCP-compatible client (Claude, Cursor, etc.)
Optional: Docker for containerized deployment
🔧 Installation Options
Option 1: Smithery (Recommended for Cursor)
Automatically configures MCP client:
npx -y @smithery/cli install @emmahyde/thinking-patterns --client cursorOption 2: NPM Package
For local development:
npm install @emmahyde/thinking-patternsOption 3: NPX (Zero Installation)
Run without installing:
npx -y @emmahyde/thinking-patternsOption 4: Docker
# Build image
docker build -t thinking-patterns .
# Run container
docker run -it thinking-patternsOption 5: Development Setup
git clone https://github.com/emmahyde/thinking-patterns
cd thinking-patterns
npm install
npm run build
npm start🎯 Use Cases
Software Development
Debug production issues systematically
Decompose complex features into user stories
Review code with multiple perspectives
Plan architecture changes step-by-step
Business Strategy
Make data-driven decisions with frameworks
Apply mental models to strategic planning
Model business processes over time
Optimize resource allocation
Research & Analysis
Test hypotheses with scientific method
Evaluate arguments critically
Model new domains systematically
Analyze temporal patterns
Creative Problem Solving
Use visual reasoning for design challenges
Apply recursive thinking to complex patterns
Optimize solutions with probabilistic algorithms
Generate innovative solutions with mental models
🔗 Integration Examples
npx
{
"mcpServers": {
"thinking-patterns": {
"command": "npx",
"args": ["-y", "@emmahyde/thinking-patterns"]
}
}
}Local Development
{
"mcpServers": {
"thinking-patterns": {
"command": "node",
"args": ["/path/to/thinking-patterns/dist/index.js"]
}
}
}📊 Tool Categories
Category | Tools | Best For |
Systematic | sequential_thinking, problem_decomposition, recursive_thinking | Planning, breaking down complexity |
Mental Models | mental_model, decision_framework, domain_modeling | Strategic thinking, decision making |
Scientific | scientific_method, critical_thinking, debugging_approach | Analysis, troubleshooting, validation |
Collaborative | collaborative_reasoning, structured_argumentation | Team thinking, debate, consensus |
Advanced | metacognitive_monitoring, visual_reasoning, temporal_thinking | Self-reflection, design, process modeling |
Probabilistic | stochastic_algorithm | Optimization, uncertainty, ML/AI |
🤝 Contributing
Contributions welcome! Please see our Contributing Guide for details.
📄 License
MIT License - see LICENSE for details.
🙏 Acknowledgments
Built on the Model Context Protocol (MCP) by Anthropic
Mental Models framework inspired by cognitive science research
Stochastic algorithms based on reinforcement learning and decision theory
📞 Support
📖 Tool Reference - Complete API documentation
💡 Examples Guide - Usage examples and patterns
Give an AI thinking patterns, and it can solve any problem systematically.
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