mcts-mcp-server

[](https://mseep.ai/app/angrysky56-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 - **Multi-LLM Support**: Supports Ollama, OpenAI, Anthropic, and Google Gemini models. ## Quick Start Installation The MCTS MCP Server now includes cross-platform setup scripts that work on Windows, macOS, and Linux. ### Prerequisites - **Python 3.10+** (required) - **Internet connection** (for downloading dependencies) ### Automatic Setup **Option 1: Cross-platform Python setup (Recommended)** ```bash # Clone the repository git clone https://github.com/angrysky56/mcts-mcp-server.git cd mcts-mcp-server # Run the setup script python setup.py ``` **Option 2: Platform-specific scripts** **Linux/macOS:** ```bash chmod +x setup.sh ./setup.sh ``` **Windows:** ```cmd setup_windows.bat ``` ### What the Setup Does The setup script automatically: 1. ✅ Checks Python version compatibility (3.10+ required) 2. ✅ Installs the UV package manager (if not present) 3. ✅ Creates a virtual environment 4. ✅ Installs all dependencies including google-genai 5. ✅ Creates `.env` file from template 6. ✅ Generates Claude Desktop configuration 7. ✅ Creates state directories 8. ✅ Verifies the installation ### Verify Installation After setup, verify everything works: ```bash python verify_installation.py ``` This runs comprehensive checks and tells you if anything needs fixing. ## Configuration ### 1. API Keys Setup Edit the `.env` file created during setup: ```env # Add your API keys (remove quotes and add real keys) OPENAI_API_KEY=sk-your-openai-key-here ANTHROPIC_API_KEY=sk-ant-your-anthropic-key-here GEMINI_API_KEY=your-gemini-api-key-here # Set default provider and model (optional) DEFAULT_LLM_PROVIDER=gemini DEFAULT_MODEL_NAME=gemini-2.0-flash ``` **Getting API Keys:** - **OpenAI**: https://platform.openai.com/api-keys - **Anthropic**: https://console.anthropic.com/ - **Google Gemini**: https://aistudio.google.com/app/apikey - **Ollama**: No API key needed (local models) ### 2. Claude Desktop Integration The setup creates `claude_desktop_config.json`. Add its contents to your Claude Desktop config: **Linux/macOS:** ```bash # Config location ~/.config/claude/claude_desktop_config.json ``` **Windows:** ```cmd # Config location %APPDATA%\Claude\claude_desktop_config.json ``` **Example config structure:** ```json { "mcpServers": { "mcts-mcp-server": { "command": "uv", "args": [ "--directory", "/path/to/mcts-mcp-server/src", "run", "mcts-mcp-server" ], "env": { "UV_PROJECT_ENVIRONMENT": "/path/to/mcts-mcp-server" } } } } ``` **Important:** Update the paths to match your installation directory. ### 3. Restart Claude Desktop After adding the configuration, restart Claude Desktop to load the MCTS server. ## Usage The server exposes many tools to your LLM detailed below in a copy-pasteable format for your system prompt. 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 and analysis tools.  ## How It Works The MCTS MCP server 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: 1. Initializes the MCTS system with the question 2. Runs multiple iterations of exploration using the MCTS algorithm 3. Generates deterministic responses for various analytical tasks 4. Returns the best analysis found during the search ## Manual Installation (Advanced) If you prefer manual setup or the automatic setup fails: ### 1. Install UV Package Manager **Linux/macOS:** ```bash curl -LsSf https://astral.sh/uv/install.sh | sh ``` **Windows (PowerShell):** ```powershell powershell -c "irm https://astral.sh/uv/install.ps1 | iex" ``` ### 2. Setup Project ```bash # Clone repository git clone https://github.com/angrysky56/mcts-mcp-server.git cd mcts-mcp-server # Create virtual environment uv venv .venv # Activate virtual environment # Linux/macOS: source .venv/bin/activate # Windows: .venv\Scripts\activate # Install dependencies uv pip install . uv pip install .[dev] # Optional development dependencies # Install Gemini package specifically (if not in pyproject.toml) uv pip install google-genai>=1.20.0 ``` ### 3. Create Configuration Files ```bash # Copy environment file cp .env.example .env # Edit .env file with your API keys nano .env # or use your preferred editor # Create state directory mkdir -p ~/.mcts_mcp_server ``` ## Troubleshooting ### Common Issues **1. Python Version Error** ``` Solution: Install Python 3.10+ from python.org ``` **2. UV Not Found After Install** ```bash # Add UV to PATH manually export PATH="$HOME/.cargo/bin:$PATH" # Or on Windows: Add %USERPROFILE%\.cargo\bin to PATH ``` **3. Google Gemini Import Error** ```bash # Install Gemini package manually uv pip install google-genai ``` **4. Permission Denied (Linux/macOS)** ```bash # Make scripts executable chmod +x setup.sh setup_unix.sh ``` **5. Claude Desktop Not Detecting Server** - Verify config file location and syntax - Check that paths in config are absolute and correct - Restart Claude Desktop completely - Check Claude Desktop logs for errors ### Getting Help 1. **Run verification**: `python verify_installation.py` 2. **Check logs**: Look at Claude Desktop's developer tools 3. **Test components**: Run individual tests in the repository 4. **Review documentation**: Check USAGE_GUIDE.md for detailed instructions ## API Key Management For using LLM providers like OpenAI, Anthropic, and Google Gemini, you need to provide API keys. This server loads API keys from a `.env` file located in the root of the repository. 1. **Copy the example file**: `cp .env.example .env` 2. **Edit `.env`**: Open the `.env` file and replace the placeholder keys with your actual API keys: ```env OPENAI_API_KEY="your_openai_api_key_here" ANTHROPIC_API_KEY="your_anthropic_api_key_here" GEMINI_API_KEY="your_google_gemini_api_key_here" ``` 3. **Set Defaults (Optional)**: You can also set the default LLM provider and model name in the `.env` file: ```env # Default LLM Provider to use (e.g., "ollama", "openai", "anthropic", "gemini") DEFAULT_LLM_PROVIDER="ollama" # Default Model Name for the selected provider DEFAULT_MODEL_NAME="cogito:latest" ``` If these are not set, the system defaults to "ollama" and attempts to use a model like "cogito:latest" or another provider-specific default. The `.env` file is included in `.gitignore`, so your actual keys will not be committed to the repository. ## Suggested System Prompt and Updated tools --- ```markdown # MCTS server and usage instructions: # List available Ollama models (if using Ollama) list_ollama_models() # Set the active LLM provider and model # provider_name can be "ollama", "openai", "anthropic", "gemini" # model_name is specific to the provider (e.g., "cogito:latest" for ollama, "gpt-4" for openai) set_active_llm(provider_name="openai", model_name="gpt-3.5-turbo") # Or, to use defaults from .env or provider-specific defaults: # set_active_llm(provider_name="openai") # Initialize analysis (can also specify provider and model here to override active settings for this run) initialize_mcts(question="Your question here", chat_id="unique_id", provider_name="openai", model_name="gpt-4") # Or using the globally set active LLM: # initialize_mcts(question="Your question here", chat_id="unique_id") run_mcts(iterations=1, simulations_per_iteration=5) After run_mcts is called it can take quite a long time ie minutes to hours - so you may discuss any ideas or questions or await user confirmation of the process finishing, - then proceed to synthesis and analysis tools on resumption of chat. ## MCTS-MCP Tools Overview ### Core MCTS Tools: - `initialize_mcts`: Start a new MCTS analysis with a specific question. Can optionally specify `provider_name` and `model_name` to override defaults for this run. - `run_mcts`: Run the MCTS algorithm for a set number of iterations/simulations. - `generate_synthesis`: Generate a final summary of the MCTS results. - `get_config`: View current MCTS configuration parameters, including active LLM provider and model. - `update_config`: Update MCTS configuration parameters (excluding provider/model, use `set_active_llm` for that). - `get_mcts_status`: Check the current status of the MCTS system. - `set_active_llm(provider_name: str, model_name: Optional[str])`: Select which LLM provider and model to use for MCTS. - `list_ollama_models()`: Show all available local Ollama models (if using Ollama provider). Default configuration prioritizes speed and exploration, but you can customize parameters like exploration_weight, beta_prior_alpha/beta, surprise_threshold. ## 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. ### Results Collection: - Automatically stores results in `/home/ty/Repositories/ai_workspace/mcts-mcp-server/results` (path might be system-dependent or configurable) - Organizes by provider, model name, and run ID - Stores metrics, progress info, and final outputs # MCTS Analysis Tools This extension adds powerful analysis tools to the MCTS-MCP Server, making it easy to extract insights and understand results from your MCTS runs. The MCTS Analysis Tools provide a suite of integrated functions to: 1. List and browse MCTS runs 2. Extract key concepts, arguments, and conclusions 3. Generate comprehensive reports 4. Compare results across different runs 5. Suggest improvements for better performance ## Available Run Analysis Tools ### Browsing and Basic Information - `list_mcts_runs(count=10, model=None)`: List recent MCTS runs with key metadata - `get_mcts_run_details(run_id)`: Get detailed information about a specific run - `get_mcts_solution(run_id)`: Get the best solution from a run ### Analysis and Insights - `analyze_mcts_run(run_id)`: Perform a comprehensive analysis of a run - `get_mcts_insights(run_id, max_insights=5)`: Extract key insights from a run - `extract_mcts_conclusions(run_id)`: Extract conclusions from a run - `suggest_mcts_improvements(run_id)`: Get suggestions for improvement ### Reporting and Comparison - `get_mcts_report(run_id, format='markdown')`: Generate a comprehensive report (formats: 'markdown', 'text', 'html') - `get_best_mcts_runs(count=5, min_score=7.0)`: Get the best runs based on score - `compare_mcts_runs(run_ids)`: Compare multiple runs to identify similarities and differences ## Usage Examples # To list your recent MCTS runs: list_mcts_runs() # To get details about a specific run: get_mcts_run_details('ollama_cogito:latest_1745979984') # Example run_id format ### Extracting Insights # To get key insights from a run: get_mcts_insights(run_id='ollama_cogito:latest_1745979984') ### Generating Reports # To generate a comprehensive markdown report: get_mcts_report(run_id='ollama_cogito:latest_1745979984', format='markdown') ### Improving Results # To get suggestions for improving a run: suggest_mcts_improvements(run_id='ollama_cogito:latest_1745979984') ### Comparing Runs To compare multiple runs: compare_mcts_runs(['ollama_cogito:latest_1745979984', 'openai_gpt-3.5-turbo_1745979584']) # Example run_ids ## Understanding the Results The analysis tools extract several key elements from MCTS runs: 1. **Key Concepts**: The core ideas and frameworks in the analysis 2. **Arguments For/Against**: The primary arguments on both sides of a question 3. **Conclusions**: The synthesized conclusions or insights from the analysis 4. **Tags**: Automatically generated topic tags from the content ## Troubleshooting If you encounter any issues with the analysis tools: 1. Check that your MCTS run completed successfully (status: "completed") 2. Verify that the run ID you're using exists and is correct 3. Try listing all runs to see what's available: `list_mcts_runs()` 4. Make sure the `.best_solution.txt` file exists in the run's directory ## Advanced Example Usage ### Customizing Reports You can generate reports in different formats: # Generate a markdown report report = get_mcts_report(run_id='ollama_cogito:latest_1745979984', format='markdown') # Generate a text report report = get_mcts_report(run_id='ollama_cogito:latest_1745979984', format='text') # Generate an HTML report report = get_mcts_report(run_id='ollama_cogito:latest_1745979984', format='html') ### Finding the Best Runs To find your best-performing runs: best_runs = get_best_mcts_runs(count=3, min_score=8.0) This returns the top 3 runs with a score of at least 8.0. ## Simple Usage Instructions 1. **Setting the LLM Provider and Model**: # For Ollama: list_ollama_models() # See available Ollama models set_active_llm(provider_name="ollama", model_name="cogito:latest") # For OpenAI: set_active_llm(provider_name="openai", model_name="gpt-4") # For Anthropic: set_active_llm(provider_name="anthropic", model_name="claude-3-opus-20240229") # For Gemini: set_active_llm(provider_name="gemini", model_name="gemini-1.5-pro-latest") 2. **Starting a New Analysis**: # Uses the LLM set by set_active_llm, or defaults from .env initialize_mcts(question="Your question here", chat_id="unique_identifier") # Alternatively, specify provider/model for this specific analysis: # initialize_mcts(question="Your question here", chat_id="unique_identifier", provider_name="openai", model_name="gpt-4-turbo") 3. **Running the Analysis**: run_mcts(iterations=3, simulations_per_iteration=10) 4. **Comparing Performance (Ollama specific example)**: run_model_comparison(question="Your question", iterations=2) 5. **Getting Results**: generate_synthesis() # Final summary of results get_mcts_status() # Current status and metrics ``` --- ### 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"  ## For Developers ### Development Setup ```bash # Activate virtual environment source .venv/bin/activate # Install development dependencies uv pip install .[dev] # 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: ```bash # Activate the virtual environment source .venv/bin/activate # Run the verification script python verify_installation.py # Run the test script python test_server.py ``` This will test the LLM adapter to ensure it's working properly. ### Project Structure ``` mcts-mcp-server/ ├── src/mcts_mcp_server/ # Main package │ ├── adapters/ # LLM adapters │ ├── analysis_tools/ # Analysis and reporting tools │ ├── mcts_core.py # Core MCTS algorithm │ ├── tools.py # MCP tools │ └── server.py # MCP server ├── setup.py # Cross-platform setup script ├── setup.sh # Unix setup script ├── setup_windows.bat # Windows setup script ├── verify_installation.py # Installation verification ├── pyproject.toml # Project configuration ├── .env.example # Environment template └── README.md # This file ``` ## 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 ### Development Workflow 1. **Fork the repository** 2. **Run setup**: `python setup.py` 3. **Verify installation**: `python verify_installation.py` 4. **Make changes** 5. **Test changes**: `python test_server.py` 6. **Submit pull request** # License: [MIT](https://github.com/angrysky56/mcts-mcp-server/blob/main/LICENSE)