get_synthesis
Produce a final synthesis of Monte Carlo Tree Search (MCTS) results, enabling comprehensive analysis of topics, questions, or text inputs for decision-making.
Instructions
Generate a final synthesis of the MCTS results
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Implementation Reference
- src/mcts_mcp_server/server.py:356-409 (handler)The primary handler function for the 'get_synthesis' tool. It generates a final comprehensive synthesis using the LLM based on the best MCTS analysis, checks prerequisites, and returns structured results or errors.
async def get_synthesis() -> dict[str, Any]: """ Generate a final synthesis of the MCTS results. Creates a comprehensive summary that synthesizes the key insights from the best analysis found during the MCTS search process. Returns: Dict containing the synthesis text, best score, and metadata Raises: Exception: If synthesis generation fails or MCTS hasn't been run yet """ if not server_state["initialized"]: return {"error": "MCTS not initialized. Call initialize_mcts first.", "status": "error"} if server_state["best_score"] == 0.0: return {"error": "No analysis completed yet. Run run_mcts_search first.", "status": "error"} try: question = server_state["current_question"] best_analysis = server_state["best_analysis"] best_score = server_state["best_score"] synthesis_prompt = f"""Create a comprehensive synthesis based on this MCTS analysis: Original Question: {question} Best Analysis Found (Score: {best_score}/10): {best_analysis} Provide a final synthesis that: 1. Summarizes the key insights 2. Highlights the most important findings 3. Offers actionable conclusions 4. Explains why this approach is valuable Make it clear, comprehensive, and practical.""" synthesis = await call_llm(synthesis_prompt) return { "synthesis": synthesis, "best_score": best_score, "iterations_completed": server_state["iterations_completed"], "question": question, "provider": server_state["provider"], "model": server_state["model"], "status": "success" } except Exception as e: logger.error(f"Error generating synthesis: {e}") return {"error": f"Synthesis failed: {e!s}", "status": "error"} - src/mcts_mcp_server/server.py:110-114 (registration)Registration of the 'get_synthesis' tool in the @server.list_tools() handler, defining its name, description, and empty input schema (no parameters required).
types.Tool( name="get_synthesis", description="Generate a final synthesis of the MCTS results", inputSchema={"type": "object", "properties": {}} ), - Input schema for the 'get_synthesis' tool, which is an empty object indicating no input parameters are required.
inputSchema={"type": "object", "properties": {}} - src/mcts_mcp_server/server.py:154-155 (registration)Dispatch logic in the @server.call_tool() handler that routes calls to the get_synthesis function.
elif name == "get_synthesis": result = await get_synthesis()