MCTS MCP Server

# 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

{ "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" } } } }

MCTS server and usage instructions: MCTS server and usage instructions: list_ollama_models() # Check what models are available set_ollama_model("cogito:latest") # Set the model you want to use initialize_mcts(question="Your question here", chat_id="unique_id") # Initialize analysis run_mcts(iterations=1, simulations_per_iteration=5) # Run the analysis After run_mcts is called it can take wuite 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 - `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 - `update_config`: Update MCTS configuration parameters - `get_mcts_status`: Check the current status of the MCTS system 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. ### Ollama Integration Tools: - `list_ollama_models`: Show all available local Ollama models - `set_ollama_model`: Select which Ollama model to use for MCTS - `run_model_comparison`: Run the same MCTS process across multiple models ### Results Collection: - Automatically stores results in `/home/ty/Repositories/ai_workspace/mcts-mcp-server/results` - Organizes by 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('cogito:latest_1745979984') ### Extracting Insights # To get key insights from a run: get_mcts_insights(run_id='cogito:latest_1745979984') ### Generating Reports # To generate a comprehensive markdown report: get_mcts_report(run_id='cogito:latest_1745979984', format='markdown') ### Improving Results # To get suggestions for improving a run: suggest_mcts_improvements(run_id='cogito:latest_1745979984') ### Comparing Runs To compare multiple runs: compare_mcts_runs(['cogito:latest_1745979984', 'qwen3:0.6b_1745979584']) ## 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='cogito:latest_1745979984', format='markdown') # Generate a text report report = get_mcts_report(run_id='cogito:latest_1745979984', format='text') # Generate an HTML report report = get_mcts_report(run_id='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. **Changing Models**: list_ollama_models() # See available models set_ollama_model("qwen3:0.6b") # Set to fast small model 2. **Starting a New Analysis**: initialize_mcts(question="Your question here", chat_id="unique_identifier") 3. **Running the Analysis**: run_mcts(iterations=3, simulations_per_iteration=10) 4. **Comparing Performance**: run_model_comparison(question="Your question", iterations=2) 5. **Getting Results**: generate_synthesis() # Final summary of results get_mcts_status() # Current status and metrics

# 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

# Activate the virtual environment source .venv/bin/activate # Run the test script python test_server.py