Stochastic Thinking MCP Server

# Stochastic Thinking MCP Server [](https://smithery.ai/server/@chirag127/stochastic-thinking-mcp-server)  A Model Context Protocol (MCP) server that provides stochastic algorithms and probabilistic decision-making capabilities, extending sequential thinking with advanced mathematical models. *Last updated: May 17, 2025 22:30:57 UTC* ## 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?" ## 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 ## 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](https://smithery.ai/server/@chirag127/stochastic-thinking-mcp-server): ```bash npx -y @smithery/cli install @chirag127/stochastic-thinking-mcp-server --client claude ``` ### Manual Installation ```bash # 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 The server exposes a single tool called `stochasticalgorithm` that can be used to apply various stochastic algorithms to decision-making problems. Example usage: ```json { "algorithm": "mdp", "problem": "Optimize route selection for delivery vehicles", "parameters": { "states": 10, "gamma": 0.95, "learningRate": 0.1 } } ``` ## License MIT ## Author Chirag Singhal (chirag127)