MCTS MCP Server

# MCTS MCP Server A Model Context Protocol (MCP) server that exposes an Advanced Bayesian Monte Carlo Tree Search (MCTS) engine for AI-assisted analysis and reasoning. ## Overview This MCP server enables Claude to use Monte Carlo Tree Search (MCTS) algorithms for deep, explorative analysis of topics, questions, or text inputs. The MCTS algorithm uses a Bayesian approach to systematically explore different angles and interpretations, producing insightful analyses that evolve through multiple iterations. ## Features - **Bayesian MCTS**: Uses a probabilistic approach to balance exploration vs. exploitation during analysis - **Multi-iteration Analysis**: Supports multiple iterations of thinking with multiple simulations per iteration - **State Persistence**: Remembers key results, unfit approaches, and priors between turns in the same chat - **Approach Taxonomy**: Classifies generated thoughts into different philosophical approaches and families - **Thompson Sampling**: Can use Thompson sampling or UCT for node selection - **Surprise Detection**: Identifies surprising or novel directions of analysis - **Intent Classification**: Understands when users want to start a new analysis or continue a previous one ## Installation The setup uses UV (Astral UV), a faster alternative to pip that offers improved dependency resolution. 1. Ensure you have Python 3.10+ installed 2. Run the setup script: ```bash cd /home/ty/Repositories/ai_workspace/mcts-mcp-server ./setup.sh ``` This will: - Install UV if not already installed - Create a virtual environment with UV - Install the required packages using UV - Create the necessary state directory Alternatively, you can manually set up: ```bash # Install UV if not already installed curl -fsSL https://astral.sh/uv/install.sh | bash # Create and activate a virtual environment cd /home/ty/Repositories/ai_workspace/mcts-mcp-server uv venv .venv source .venv/bin/activate # Install dependencies uv pip install -r requirements.txt ``` ## Claude Desktop Integration To integrate with Claude Desktop: 1. Copy the `claude_desktop_config.json` example from this repository 2. Add it to your Claude Desktop configuration (typically located at `~/.claude/claude_desktop_config.json`) 3. Ensure the paths in the configuration point to the correct location on your system ## Usage The server exposes the following tools to Claude: - `initialize_mcts`: Start a new MCTS analysis with a given question - `run_mcts`: Run the MCTS algorithm for a specified number of iterations - `generate_synthesis`: Generate a final synthesis of the MCTS results - `get_config`: View the current MCTS configuration - `update_config`: Update the MCTS configuration - `get_mcts_status`: Get the current status of the MCTS system When you ask Claude to perform deep analysis on a topic or question, it will leverage these tools automatically to explore different angles using the MCTS algorithm. ### Example Prompts - "Analyze the implications of artificial intelligence on human creativity" - "Continue exploring the ethical dimensions of this topic" - "What was the best analysis you found in the last run?" - "How does this MCTS process work?" - "Show me the current MCTS configuration" ## Development For development and testing: ```bash # Activate virtual environment source .venv/bin/activate # Run the server directly (for testing) uv run server.py # OR use the MCP CLI tools uv run -m mcp dev server.py ``` ## Configuration You can customize the MCTS parameters in the config dictionary or through Claude's `update_config` tool. Key parameters include: - `max_iterations`: Number of MCTS iterations to run - `simulations_per_iteration`: Number of simulations per iteration - `exploration_weight`: Controls exploration vs. exploitation balance (in UCT) - `early_stopping`: Whether to stop early if a high-quality solution is found - `use_bayesian_evaluation`: Whether to use Bayesian evaluation for node scores - `use_thompson_sampling`: Whether to use Thompson sampling for selection