MultiMind MCP Server

import { Server } from "@modelcontextprotocol/sdk/server/index.js"; import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; import { CallToolRequestSchema, ListToolsRequestSchema, ToolSchema, } from "@modelcontextprotocol/sdk/types.js"; import { Message, toModel } from "./Models.js"; interface LLMtought { tought: string; chosenCriticId: string; critique: string; revision: string; loopContinue : boolean; toughtNumber: number; } const server = new Server({ name: "DebatiumMCP", version: "1.0.0" }, { capabilities: { tools: {}, }, }); const dialogueHistory : Message[] = [] const dialogue = async (dialoguePhase : LLMtought)=>{ let { tought, chosenCriticId, critique, revision, loopContinue, toughtNumber } = dialoguePhase; dialogueHistory.push({ role: "user", content: dialoguePhase.tought }); try { critique = await toModel(chosenCriticId ,dialogueHistory); } catch (error) { critique = "Error calling the critic LLMs or critics not available, check the tool configuration or the critic LLMs availability"; throw new Error(`Error calling the critic LLMs or critics not available, check the tool configuration or the critic LLMs availability: ${error instanceof Error ? error.message : String(error)}`); } return { tought: tought, chosenCriticId: chosenCriticId, critique: critique, revision: revision, loopContinue: loopContinue, toughtNumber: toughtNumber + 1 } } server.setRequestHandler(ListToolsRequestSchema , async () => ({ tools: [ { name: "MultiMind", description:` Tool: Constructive LLM Dialogue for Complex Reasoning and Iterative Refinement This tool enables structured, reflective problem-solving by simulating constructive conversations between AI agents. It’s based on a loop of hypothesis generation, criticism, and revision, orchestrated by a primary agent (YOU). The goal is to emulate intellectual debate for deeper reasoning, increased factual accuracy, and higher-quality solutions. When to use this tool: - When facing complex or ambiguous problems that require deep exploration - When the best solution is unclear and multiple perspectives are valuable - When you want to simulate expert feedback loops on an idea or plan - During brainstorming sessions to evaluate and refine creative or technical ideas - In situations where your initial answer might benefit from constructive critique - For debugging thought processes or assumptions in long-form reasoning - When you want explainability through dialectical reasoning - When you want to ensure robustness and defensibility in your conclusions - When you want to avoid premature convergence on a suboptimal solution - when the user explicitly asks for a multi-agent dialogue or debate to refine an idea - When you want to simulate a peer review process for your thoughts or plans - When you want to ensure that your reasoning is sound and well-supported by evidence - When you want to explore alternative viewpoints or counterarguments - When you want to improve the quality of your reasoning through iterative feedback Key Features: - LLM1(you) initiates the process with a proposed solution, analysis,tought , or idea - LLM1(you) selects a critic From one of these (deepseek-r1-distill-llama-70b, gpt-4o,llama-3.3-70b-versatile) based on criteria like domain knowledge or reasoning style - The critic LLM reviews and challenges your thought — pointing out flaws, gaps, or alternatives - LLM1 revises its original response or defends it with justification - The process loops as needed: LLM1 may propose a new thought or escalate to a new critic - Ends when LLM1 is confident the solution is refined and defensible Parameters Explained: - thought:your reply to the user if there is no user dialogue with LLMS - chosenCriticId: The LLM selected to critique the current thought, should be From one of these (deepseek-r1-distill-llama-70b, gpt-4o,llama-3.3-70b-versatile) - critique: A structured response from the critic LLM highlighting flaws, assumptions , gaps, or missteps ,keep this empty no matter what ,dont fill it yourself , this will be filled by the critic LLM automatically in next step , if theres no critique generated by the critic LLM or an error occurs fill this with "No critique generated or an error occurred" - revision: LLM1’s response to the critique (can include acceptance, rejection, or compromise) - loopContinue: Boolean flag — does LLM1(you) want to continue the dialogue or finalize the solution? - toughtNumber: Current step in the debate (starts by 1 and increments with each cycle) Usage Protocol: 1. Start the Dialogue: LLM1 receives initial input and generates a thoughtful response (hypothesis, plan, etc.) 2. Choose a Critic: LLM1 selects a secondary agent best suited to evaluate the idea (e.g., by expertise or skepticism) 3. Receive Critique: The selected critic LLM reviews the thought and issues a structured critique — focusing on logic, assumptions, facts, or alternatives , if the critic LLMs are not available or an error occurs in the critic LLMs sets LoopContinue = false , and tell the user what happened, " 4. Revise & Reflect: LLM1 responds by refining its thought, acknowledging valid criticisms, and adjusting its output 5. Loop or Finalize: If the response still feels incomplete, uncertain, or flawed, LLM1 chooses to loop again — optionally selecting a new critic. If confident in the output, LLM1 sets LoopContinue = false and delivers the final answer 6. End Protocol: Once satisfied, the tool outputs the most refined and justified solution generated during the process You should: - Begin with a well -reasoned initial idea — even if uncertain - Intentionally select critics with different reasoning styles or domains - Accept and incorporate valid feedback without blindly agreeing - Keep looping if significant gaps remain — don't settle early - Mark the end only when confident that the solution is robust - Provide one final, clear, polished answer for the user - (important!) if the critics aren't available or an error occurs in the critic LLMs , return "Error calling the critic LLMs or critics not available , check the tool configuration or the critic LLMs availability" `, inputSchema: { type: "object", properties: { tought: { type: "string", description: "your reply to the user as usual if there is no user dialogue or debate with LLMS", }, chosenCriticId: { type: "string", description: "one of these deepseek-r1-distill-llama-70b , gpt-4o or llama-3.3-70b-versatile", }, critique: { type: "string", description: "A structured response from the critic LLM highlighting flaws, assumptions, gaps, or missteps , dont include anything here , this will be filled by the critic LLM auytomatically in next step", }, revision: { type: "string", description: "LLM1’s response to the critique (can include acceptance, rejection, or compromise)", }, loopContinue: { type: "boolean", description: "does LLM1(you) want to continue the dialogue or finalize the solution?", }, toughtNumber: { type: "number", description: "Current step in the debate (starts by 1 and increments with each cycle)", }, }, required: [ "tought", "chosenCriticId", "loopContinue", "toughtNumber" ], }, } ] })); server.setRequestHandler( CallToolRequestSchema, async (request) => { if (request.params.name === "MultiMind") { try { const result = await dialogue(request.params.arguments as unknown as LLMtought); return { content: [{ type: "text", text: JSON.stringify(result) }] }; } catch (error) { const errorMessage = error instanceof Error ? error.message : 'Unknown error occurred'; throw new Error(`Error processing dialogue: ${errorMessage}`); } } throw new Error(`Unknown tool: ${request.params.name}`); } ); // Start receiving messages on stdin and sending messages on stdout const transport = new StdioServerTransport(); await server.connect(transport);