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AzDeltaQQ
by AzDeltaQQ

reason_mcts

Solve complex problems or tasks by applying Monte Carlo Tree Search (MCTS) through the MCP Advanced Reasoning Server, enhancing decision-making and reasoning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe problem or task to reason about using MCTS

Implementation Reference

  • The handler function that implements the core logic of the 'reason_mcts' tool. It simulates a 3-step MCTS reasoning process, generating thoughts iteratively and returns a structured JSON response containing all thoughts.
    async ({ query }) => { try { // Initialize first thought const totalThoughts = 3; let currentThought = `MCTS Reasoning (Step 1/${totalThoughts}): Initial analysis of the problem "${query}":\n\n` + `First, let me understand what we're trying to solve here. ${query}\n\n` + `[This would be the initial MCTS-based reasoning]`; let thoughtNumber = 1; let complete = false; let allThoughts = [currentThought]; // Automatically iterate through all thoughts while (!complete && thoughtNumber < totalThoughts) { // Simulate next thought generation thoughtNumber++; const nextThought = `MCTS Reasoning (Step ${thoughtNumber}/${totalThoughts}): ` + `Based on previous analysis "${currentThought.slice(0, 50)}...", ` + `further exploration with 50 simulations suggests...\n\n` + `[This would be the next step of MCTS-based reasoning for: ${query}]`; allThoughts.push(nextThought); currentThought = nextThought; // Check if we've reached the final thought if (thoughtNumber >= totalThoughts) { complete = true; } } // Final result with all thoughts return { content: [ { type: "text", text: JSON.stringify({ strategy: "mcts", originalPrompt: query, allThoughts: allThoughts, thoughtNumber: thoughtNumber, totalThoughts: totalThoughts, complete: true }, null, 2) } ] }; } catch (error) { throw new ReasoningError(`MCTS reasoning command failed: ${error instanceof Error ? error.message : String(error)}`); } }
  • Input schema for the 'reason_mcts' tool using Zod, defining a single 'query' string parameter.
    { query: z.string().describe("The problem or task to reason about using MCTS") },
  • The server.tool() call that registers the 'reason_mcts' tool, including its name, input schema, and handler function.
    server.tool( "reason_mcts", { query: z.string().describe("The problem or task to reason about using MCTS") }, async ({ query }) => { try { // Initialize first thought const totalThoughts = 3; let currentThought = `MCTS Reasoning (Step 1/${totalThoughts}): Initial analysis of the problem "${query}":\n\n` + `First, let me understand what we're trying to solve here. ${query}\n\n` + `[This would be the initial MCTS-based reasoning]`; let thoughtNumber = 1; let complete = false; let allThoughts = [currentThought]; // Automatically iterate through all thoughts while (!complete && thoughtNumber < totalThoughts) { // Simulate next thought generation thoughtNumber++; const nextThought = `MCTS Reasoning (Step ${thoughtNumber}/${totalThoughts}): ` + `Based on previous analysis "${currentThought.slice(0, 50)}...", ` + `further exploration with 50 simulations suggests...\n\n` + `[This would be the next step of MCTS-based reasoning for: ${query}]`; allThoughts.push(nextThought); currentThought = nextThought; // Check if we've reached the final thought if (thoughtNumber >= totalThoughts) { complete = true; } } // Final result with all thoughts return { content: [ { type: "text", text: JSON.stringify({ strategy: "mcts", originalPrompt: query, allThoughts: allThoughts, thoughtNumber: thoughtNumber, totalThoughts: totalThoughts, complete: true }, null, 2) } ] }; } catch (error) { throw new ReasoningError(`MCTS reasoning command failed: ${error instanceof Error ? error.message : String(error)}`); } } );
  • src/index.ts:23-23 (registration)
    Invocation of registerCommandWrappers on the MCP server instance, which in turn registers the 'reason_mcts' tool.
    registerCommandWrappers(server);

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