MCP 服务器

ThoughtMCP

Implements human-like cognitive architecture for enhanced AI reasoning through dual-process thinking, memory systems, emotional processing, and metacognitive monitoring. Enables users to process thoughts with biological-like cognitive processes including intuitive and deliberative reasoning modes.

README

ThoughtMCP

AI that thinks more like humans do.

ThoughtMCP gives AI systems human-like thinking capabilities. Instead of just processing text, it can think systematically, remember experiences, and check its own reasoning quality.

🚀 Production Ready: 789 tests, 79.63% coverage, stable API, ready for real-world use.

What Makes It Different?

Most AI systems process text once and respond. ThoughtMCP implements multiple thinking systems inspired by cognitive science:

🧠 Human-Like Thinking

Fast intuitive responses for familiar problems
Careful deliberation for complex decisions
Creative exploration for innovation challenges
Analytical reasoning for technical problems

💾 Learning Memory

Remembers experiences and learns from them
Builds knowledge that improves over time
Recalls relevant information when making decisions
Consolidates patterns from specific cases to general principles

🔍 Self-Monitoring

Checks its own reasoning for quality and biases
Provides confidence levels for transparency
Suggests improvements to its own thinking
Adapts approach based on problem complexity

⚡ Production Ready

789 comprehensive tests with 79.63% coverage
Multiple thinking modes for different scenarios
Configurable behavior for your specific needs
Robust error handling with graceful degradation

Quick Start

1. Install and Setup

# Clone the repository
git clone https://github.com/keyurgolani/ThoughtMcp.git
cd ThoughtMcp

# Install dependencies
npm install

# Build and start
npm run build
npm run dev

2. Try Your First Example

Ask ThoughtMCP to help with a decision:

{
  "tool": "think",
  "arguments": {
    "input": "I'm trying to decide between two job offers. One pays more but has longer hours, the other has better work-life balance but lower pay. How should I approach this decision?",
    "mode": "deliberative"
  }
}

What happens:

Analyzes your question systematically
Considers multiple factors and perspectives
Provides structured reasoning with confidence levels
Suggests ways to improve the decision-making process

3. Build Knowledge Over Time

Store important insights:

{
  "tool": "remember",
  "arguments": {
    "content": "When choosing between job offers, work-life balance often matters more than salary for long-term satisfaction",
    "type": "semantic",
    "importance": 0.8
  }
}

Recall relevant knowledge:

{
  "tool": "recall",
  "arguments": {
    "cue": "job decision work-life balance"
  }
}

The Four Thinking Tools

🧠 Think - Systematic Reasoning

Process complex questions using human-like reasoning:

Intuitive mode: Fast, gut-reaction responses
Deliberative mode: Slow, careful analysis
Creative mode: Innovative problem-solving
Analytical mode: Logical, data-driven reasoning

💾 Remember - Build Knowledge

Store experiences and insights for future use:

Episodic memory: Specific experiences and events
Semantic memory: General knowledge and principles
Importance weighting: Prioritize what matters most
Emotional tagging: Remember how things felt

🔍 Recall - Find Relevant Information

Retrieve past experiences and knowledge when needed:

Similarity matching: Find related experiences
Context-aware: Consider current situation
Confidence scoring: Know how relevant results are
Cross-memory search: Search both experience and knowledge

🔬 Analyze Reasoning - Quality Control

Check thinking quality and identify potential problems:

Bias detection: Spot common reasoning errors
Logic validation: Ensure arguments are sound
Confidence assessment: Evaluate certainty levels
Improvement suggestions: Get better at reasoning

Real-World Examples

See ThoughtMCP in action with practical scenarios:

Customer Support Agent - Solving technical problems systematically
Personal Finance Advisor - Making complex financial decisions
Recipe Recommendation - Personalized suggestions with constraints
Study Buddy - Helping students learn effectively
Travel Planning - Complex multi-constraint planning

Each example shows:

The real-world problem
Step-by-step tool usage
How cognitive thinking improves outcomes
Lessons you can apply to your own use cases

Documentation

🚀 New to ThoughtMCP?

Getting Started - 5-minute tutorial and basic concepts
Installation Guide - Detailed setup instructions
Basic Concepts - How human-like thinking works
Examples - From simple to complex real-world scenarios

👩‍💻 For Developers

API Reference - Complete tool documentation and schemas
Integration Guide - Add to your applications
Configuration - Customize behavior and performance
Troubleshooting - Common issues and solutions

🧠 Understanding the Architecture

Architecture Overview - How the cognitive system works
Cognitive Components - Individual system details
Research Background - Academic foundations and algorithms
Performance Benchmarks - Speed and accuracy metrics

🛠️ Contributing

Development Setup - Set up for development
Contributing Guide - How to contribute effectively
Architecture for Developers - Codebase structure
Testing Guide - Writing and running tests

Why Choose ThoughtMCP?

For AI Applications

Better Decision Making: Considers multiple perspectives and checks reasoning quality
Continuous Learning: Gets smarter over time by remembering experiences
Transparency: Shows reasoning process and confidence levels
Adaptability: Different thinking modes for different types of problems

For Developers

Production Ready: 789 tests, comprehensive error handling, performance monitoring
Easy Integration: Standard MCP protocol, clear API, extensive documentation
Configurable: Tune behavior for your specific use case and performance needs
Open Source: MIT license, active community, extensible architecture

For Researchers

Scientifically Grounded: Based on established cognitive science research
Comprehensive Implementation: Full dual-process theory, memory systems, metacognition
Benchmarked Performance: Validated against cognitive psychology principles
Extensible Design: Add new cognitive components and reasoning strategies