@mseep/stochasticthinking

Stochastic Thinking MCP Server

Why Stochastic Thinking Matters

When AI assistants make decisions - whether writing code, solving problems, or suggesting improvements - they often fall into patterns of "local thinking", similar to how we might get stuck trying the same approach repeatedly despite poor results. This is like being trapped in a valley when there's a better solution on the next mountain over, but you can't see it from where you are.

This server introduces advanced decision-making strategies that help break out of these local patterns:

• Instead of just looking at the immediate next step (like basic Markov chains do), these algorithms can look multiple steps ahead and consider many possible futures
• Rather than always picking the most obvious solution, they can strategically explore alternative approaches that might initially seem suboptimal
• When faced with uncertainty, they can balance the need to exploit known good solutions with the potential benefit of exploring new ones

Think of it as giving your AI assistant a broader perspective - instead of just choosing the next best immediate action, it can now consider "What if I tried something completely different?" or "What might happen several steps down this path?"

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

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

Usage

Installation

Installing via Smithery

To install Stochastic Thinking MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @waldzellai/stochasticthinking --client claude

Manual Installation

npm install @waldzellai/stochasticthinking

Or run with npx:

npx @waldzellai/stochasticthinking

API Examples

Markov Decision Process

const response = await mcp.callTool("stochasticalgorithm", {
  algorithm: "mdp",
  problem: "Optimize robot navigation policy",
  parameters: {
    states: 100,
    actions: ["up", "down", "left", "right"],
    gamma: 0.9,
    learningRate: 0.1
  }
});

Monte Carlo Tree Search

const response = await mcp.callTool("stochasticalgorithm", {
  algorithm: "mcts",
  problem: "Find optimal game moves",
  parameters: {
    simulations: 1000,
    explorationConstant: 1.4,
    maxDepth: 10
  }
});

Multi-Armed Bandit

const response = await mcp.callTool("stochasticalgorithm", {
  algorithm: "bandit",
  problem: "Optimize ad placement",
  parameters: {
    arms: 5,
    strategy: "epsilon-greedy",
    epsilon: 0.1
  }
});

Bayesian Optimization

const response = await mcp.callTool("stochasticalgorithm", {
  algorithm: "bayesian",
  problem: "Hyperparameter optimization",
  parameters: {
    acquisitionFunction: "expected_improvement",
    kernel: "rbf",
    iterations: 50
  }
});

Hidden Markov Model

const response = await mcp.callTool("stochasticalgorithm", {
  algorithm: "hmm",
  problem: "Infer weather patterns",
  parameters: {
    states: 3,
    algorithm: "forward-backward",
    observations: 100
  }
});

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

Development

1. Clone the repository
2. Install dependencies: npm install
3. Build the project: npm run build
4. Start the server: npm start

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE for details.

Acknowledgments

• Based on the Model Context Protocol (MCP) by Anthropic
• Extends the sequential thinking server with stochastic capabilities
• Inspired by classic works in reinforcement learning and decision theory