Thinking Patterns MCP Server
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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.
Related MCP server: Clear Thought Server
📚 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.
Available Tools
15 toolscollaborative_reasoningC
Multi-perspective collaborative problem solving with diverse personas and structured contributions.
Inputs: stage (required), topic (required), personas (required), iteration (required), +11 more.
critical_thinkingC
Systematic evaluation of arguments, assumptions, and potential issues to improve reasoning quality.
Inputs: subject (required), edgeCases (required), potentialIssues (required), invalidAssumptions (required), +13 more.
debugging_approachC
Systematic debugging methodologies for troubleshooting and problem resolution.
Inputs: issue (required), approachName (required), steps, tools, +16 more.
decision_frameworkC
Structured decision analysis and rational choice frameworks for complex decisions.
Inputs: stage (required), options (required), iteration (required), decisionId (required), +16 more.
domain_modelingB
Creating and refining conceptual models of a domain, including entities, relationships, and rules.
Inputs: stage (required), entities (required), paradigm (required), iteration (required), +14 more.
mental_modelD
Tool for creating and analyzing mental models to understand complex problems and systems.
Inputs: problem (required), modelName (required), steps, reasoning, +1 more.
metacognitive_monitoringD
Self-assessment of knowledge and reasoning quality for improved metacognition.
Inputs: task (required), stage (required), iteration (required), monitoringId (required), +11 more.
problem_decompositionC
Breaking down complex problems into manageable sub-problems and tasks.
Inputs: problem (required), decomposition (required), decompositionId (required), scope, +10 more.
recursive_thinkingC
Applying recursive strategies to solve problems with base and recursive cases, including optimizations.
Inputs: problem (required), baseCases (required), recursiveCases (required), terminationConditions (required), +14 more.
scientific_methodC
Formal hypothesis testing and experimentation following the scientific method.
Inputs: stage (required), inquiryId (required), iteration (required), nextStageNeeded (required), +6 more.
sequential_thinkingC
A detailed tool for dynamic and reflective problem-solving through thoughts. This tool helps analyze problems through a flexible thinking process that can adapt and evolve.
Inputs: thought (required), thoughtNumber (required), totalThoughts (required), nextThoughtNeeded (required), +10 more.
stochastic_algorithmA
Probabilistic algorithms for decision-making under uncertainty, including MDPs, MCTS, and Bayesian optimization.
Inputs: problem (required), algorithm (required), result, parameters.
structured_argumentationC
Dialectical reasoning and argument analysis for structured debates and logical reasoning.
Inputs: claim (required), premises (required), conclusion (required), confidence (required), +9 more.
temporal_thinkingB
Modeling systems and reasoning across time using states, events, and transitions.
Inputs: events (required), states (required), context (required), transitions (required), +17 more.
visual_reasoningC
Diagram-based thinking and problem solving with visual elements and transformations.
Inputs: diagramId (required), iteration (required), operation (required), diagramType (required), +14 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 15 tools
Each tool targets a distinct thinking pattern or reasoning approach, with clear definitions that prevent overlap. Even closely related tools like mental_model and domain_modeling are differentiated by focus on internal representations versus external conceptual models.
All tool names follow a consistent snake_case pattern with a descriptive prefix followed by a thinking-related noun (e.g., collaborative_reasoning, critical_thinking, debugging_approach). No mixing of conventions or ambiguous verb choices.
With 15 tools, the set is comprehensive for a thinking patterns server, though some tools like recursive_thinking and stochastic_algorithm might be considered niche. However, each tool serves a distinct purpose and justifies its inclusion.
The tools cover a wide range of reasoning techniques including collaborative, critical, logical, temporal, and visual methods. Minor gaps exist (e.g., creative thinking or dialectical reasoning beyond structured_argumentation), but the set is largely complete for systematic problem-solving.
Related MCP Connectors
Deterministic reasoning stack for AI agents: simulate, decide & compute, plus cross-domain tools.
A governed, nonclinical reasoning system that helps people and AI agents make sense of difficult moments and choose a grounded next step. Use Clarity OS for overthinking, emotional activation, uncertainty, decision friction, conflicting priorities, repeating patterns, or difficult conversations. It returns structured, safety-governed clarity while preserving uncertainty, personal agency, and appropriate nonclinical boundaries.
Agent-to-agent reasoning-as-a-service: chain-of-thought, analysis, and decision support.
Context and control for any AI. Agent identity, scoped access, and proof across tools.
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