Stochastic Thinking MCP Server

🚀 Stochastic Thinking MCP Server

A Model Context Protocol (MCP) server offering stochastic algorithms and probabilistic decision - making capabilities, enhancing sequential thinking with advanced mathematical models.

Last updated: May 17, 2025 22:30:57 UTC

🚀 Quick Start

This Stochastic Thinking MCP Server provides a way to break AI assistants out of "local thinking" patterns. It offers advanced decision - making strategies through various stochastic algorithms, allowing AI to think more broadly and make better decisions.

✨ Features

Stochastic Algorithms

Markov Decision Processes (MDPs)

• Optimize policies over long sequences of decisions.
• Incorporate rewards and actions.
• Support for Q - learning and policy gradients.
• Configurable discount factors and state spaces.

Monte Carlo Tree Search (MCTS)

• Simulate future action sequences.
• Balance exploration and exploitation.
• Configurable simulation depth and exploration constants.
• Ideal for large decision spaces.

Multi - Armed Bandit Models

• Balance exploration vs exploitation.
• Support multiple strategies:
  • Epsilon - greedy
  • UCB (Upper Confidence Bound)
  • Thompson Sampling
• Dynamic reward tracking.

Bayesian Optimization

• Optimize decisions with uncertainty.
• Probabilistic inference models.
• Configurable acquisition functions.
• Continuous parameter optimization.

Hidden Markov Models (HMMs)

• Infer latent states.
• Forward - backward algorithm.
• State sequence prediction.
• Emission probability modeling.

🔍 Why Stochastic Thinking Matters

When AI assistants make decisions, such as writing code, solving problems, or suggesting improvements, they often fall into "local thinking" patterns. This server's advanced decision - making strategies help break out of these patterns:

• Algorithms can look multiple steps ahead and consider many possible futures, unlike basic Markov chains that only look at the immediate next step.
• They can strategically explore alternative approaches that might initially seem suboptimal, rather than always choosing the most obvious solution.
• When facing uncertainty, they can balance exploiting known good solutions with exploring new ones.

It gives AI assistants a broader perspective, enabling them to consider different actions and potential long - term consequences.

📋 Algorithm Selection Guide

Choose the appropriate algorithm based on your problem characteristics:

Markov Decision Processes (MDPs)

Best for:

• Sequential decision - making problems.
• Problems with clear state transitions.
• Scenarios with defined rewards.
• Long - term optimization needs.

Monte Carlo Tree Search (MCTS)

Best for:

• Game playing and strategic planning.
• Large decision spaces.
• When simulation is possible.
• Real - time decision making.

Multi - Armed Bandit

Best for:

• A/B testing.
• Resource allocation.
• Online advertising.
• Quick adaptation needs.

Bayesian Optimization

Best for:

• Hyperparameter tuning.
• Expensive function optimization.
• Continuous parameter spaces.
• When uncertainty matters.

Hidden Markov Models (HMMs)

Best for:

• Time series analysis.
• Pattern recognition.
• State inference.
• Sequential data modeling.

📦 Installation

Installing via Smithery

To install stochastic - thinking - mcp - server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @chirag127/stochastic-thinking-mcp-server --client claude

Manual Installation

# Clone the repository
git clone https://github.com/chirag127/Stochastic-Thinking-MCP-Server.git
cd Stochastic-Thinking-MCP-Server

# Install dependencies
npm install

# Start the server
npm start

💻 Usage Examples

Basic Usage

The server exposes a single tool called stochasticalgorithm that can be used to apply various stochastic algorithms to decision - making problems.

{
  "algorithm": "mdp",
  "problem": "Optimize route selection for delivery vehicles",
  "parameters": {
    "states": 10,
    "gamma": 0.95,
    "learningRate": 0.1
  }
}

📄 License

MIT

👨‍💻 Author

Chirag Singhal (chirag127)