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

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#### 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

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#### Installing via Smithery

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

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

#### Manual Installation
`bash
npm install @waldzellai/stochasticthinking
`

Or run with npx:

`bash
npx @waldzellai/stochasticthinking
`

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

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

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

#### Hidden Markov Model
`typescript
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:

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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