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

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.

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

Articulating Specific Pathways: Delving into the best_path nodes (using mcts_instance.get_best_path_nodes() if you have the instance) and examining the sequence of thought and content at each step can provide a fascinating micro-narrative of how the core insight evolved. Visualizing the tree (even a simplified version based on export_tree_summary) could also be illuminating and I will try to set up this feature.

Modifying Parameters: This is a great way to test the robustness of the finding or explore different "cognitive biases" of the system.
```
  Increasing Exploration Weight: Might lead to more diverse, less obviously connected ideas.

  Decreasing Exploration Weight: Might lead to deeper refinement of the initial dominant pathways.

  Changing Priors (if Bayesian): You could bias the system towards certain approaches (e.g., increase alpha for 'pragmatic') to see how it influences the outcome.

  More Iterations/Simulations: Would allow for potentially deeper convergence or exploration of more niche pathways.
```

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"

alt text

How It Works

The MCTS MCP server now uses a local inference approach rather than trying to call the LLM directly. This is compatible with the MCP protocol, which is designed for tools to be called by an AI assistant (like Claude) rather than for the tools to call the AI model themselves.

When Claude asks the server to perform analysis, the server:

Initializes the MCTS system with the question
Runs multiple iterations of exploration using the MCTS algorithm
Generates deterministic responses for various analytical tasks
Returns the best analysis found during the search

Installation

Clone the repository:

The setup uses UV (Astral UV), a faster alternative to pip that offers improved dependency resolution.

Ensure you have Python 3.10+ installed
Run the setup script:

./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:

# Install UV if not already installed
curl -fsSL https://astral.sh/uv/install.sh | bash

# Create and activate a virtual environment
uv venv .venv
source .venv/bin/activate

# Install dependencies
uv pip install -r requirements.txt

Claude Desktop Integration

To integrate with Claude Desktop:

Copy the contents of claude_desktop_config.json from this repository
Add it to your Claude Desktop configuration (typically located at ~/.claude/claude_desktop_config.json)
If the config file doesn't exist yet, create it and add the content from this project's claude_desktop_config.json
Restart Claude Desktop

Example configuration:

{
  "mcpServers": {
    "MCTSServer": {
      "command": "uv",
      "args": [
        "run",
        "--directory", "/home/ty/Repositories/ai_workspace/mcts-mcp-server/src/mcts_mcp_server",
        "server.py"
      ],
      "env": {
        "PYTHONPATH": "/home/ty/Repositories/ai_workspace/mcts-mcp-server"
      }
    }
  }
}

Make sure to update the paths to match the location of the MCTS MCP server on your system.

For Developers

# 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

Testing the Server

To test that the server is working correctly:

# Activate the virtual environment
source .venv/bin/activate

# Run the test script
python test_server.py

This will test the LLM adapter to ensure it's working properly.

Contributing

Contributions to improve the MCTS MCP server are welcome. Some areas for potential enhancement:

Improving the local inference adapter for more sophisticated analysis
Adding more sophisticated thought patterns and evaluation strategies
Enhancing the tree visualization and result reporting
Optimizing the MCTS algorithm parameters

License: MIT

Categories

Current Category: bayesian

mcts-mcp-server