Frequently Asked Questions (FAQ) about the MCTS MCP Server

Q: What is the MCTS MCP Server? A: The MCTS MCP Server is a Model Context Protocol (MCP) server that exposes an Advanced Bayesian Monte Carlo Tree Search (MCTS) engine for AI-assisted analysis and reasoning. It allows LLMs like Claude to perform deep, explorative analysis on topics and questions.

Q: What is MCP? A: MCP stands for Model Context Protocol. It is an open protocol that standardizes how applications provide context to LLMs, enabling AI models to interact with external data sources and tools.

Q: What are the key features of the MCTS MCP Server? A: Key features include Bayesian MCTS, multi-iteration analysis, state persistence, approach taxonomy, Thompson sampling, surprise detection, intent classification, and local Ollama model integration.

Q: How does the MCTS MCP Server work? A: When Claude asks the server to perform analysis, the server initializes the MCTS system with the question, runs multiple iterations of exploration, generates deterministic responses for various analytical tasks, and returns the best analysis found.

Q: How do I integrate the MCTS MCP Server with Claude Desktop? A: Copy the contents of claude_desktop_config.json from the repository to your Claude Desktop configuration file (typically located at ~/.claude/claude_desktop_config.json). Update the paths to match the location of the MCTS MCP server on your system.

Q: What is the purpose of the claude_desktop_config.json file? A: This configuration file tells Claude Desktop how to access and interact with the MCTS MCP Server.

Q: How do I set up the system prompt for the MCTS MCP Server? A: Configure your system prompt with the provided tools, including initialize_mcts, run_mcts, generate_synthesis, get_config, update_config, and get_mcts_status. Refer to the documentation for details on each tool’s usage.

Q: Can I customize the MCTS parameters? A: Yes, you can customize parameters such as max_iterations, simulations_per_iteration, exploration_weight, and more.

Q: What are the MCTS Analysis Tools? A: The MCTS Analysis Tools provide a suite of functions to list and browse MCTS runs, extract key concepts, generate reports, compare results, and suggest improvements.

Q: What is UBOS and how does it relate to the MCTS MCP Server? A: UBOS is a full-stack AI Agent Development Platform. The MCTS MCP Server is available on the UBOS Asset Marketplace, allowing you to easily integrate it into your AI agent workflows. UBOS provides tools for orchestrating AI agents, connecting them with enterprise data, and building custom AI agents.

Q: What are the benefits of using the MCTS MCP Server on UBOS? A: Benefits include enhanced analytical capabilities, deeper insights, improved decision-making, increased productivity, seamless integration, and customizable configuration.

Q: Where are the MCTS results stored? A: Results are automatically stored in /home/ty/Repositories/ai_workspace/mcts-mcp-server/results, organized by model name and run ID.

Q: How do I run the test script to verify the server is working correctly? A: Activate the virtual environment (source .venv/bin/activate) and then run python test_server.py.

Q: What kind of contributions are welcome for improving the MCTS MCP server? A: Contributions to improve the local inference adapter, thought patterns, evaluation strategies, tree visualization, result reporting, and MCTS algorithm parameters are welcome.

Q: What license is the MCTS MCP server released under? A: The server is released under the MIT license.

Q: How to select local Ollama models A: Use list_ollama_models() to find the right name of the model. Next step is to use set_ollama_model('model_name') to setup the model.