Polybrain MCP Server

Instructions

Input Schema

Input Schema (JSON Schema)

Implementation Reference

• src/mcp-tools.ts:43-154 (handler)

  The handler function for the 'chat' MCP tool. It manages conversation state, determines or creates conversations, validates models, sends the chat request to the OpenAI client, stores the response, and returns the result including conversation ID.
  async ({ message, conversationId, modelId, reasoning }) => { try { logger.debug("Chat tool called", { hasConversationId: !!conversationId, modelId, }); let actualConversationId: string; let actualModelId: string; // Determine conversation ID and model ID if (conversationId) { const existing = conversationManager.getConversation(conversationId); if (!existing) { return { content: [ { type: "text" as const, text: `Error: Conversation not found: ${conversationId}`, }, ], }; } if (modelId && modelId !== existing.modelId) { // Clone conversation with new model actualConversationId = conversationManager.cloneConversation(conversationId, modelId); actualModelId = modelId; } else { // Continue existing conversation actualConversationId = conversationId; actualModelId = existing.modelId; } } else { // Create new conversation actualModelId = modelId || config.models[0].id; actualConversationId = conversationManager.createConversation(actualModelId); } // Validate model exists and get its full model name const client = openaiClients.get(actualModelId); if (!client) { return { content: [ { type: "text" as const, text: `Error: Model not configured: ${actualModelId}`, }, ], }; } // Get the full model name from config for API calls const modelConfig = config.models.find((m) => m.id === actualModelId); if (!modelConfig) { return { content: [ { type: "text" as const, text: `Error: Model configuration not found: ${actualModelId}`, }, ], }; } // Add user message to conversation conversationManager.addMessage(actualConversationId, "user", message); // Get conversation history const history = conversationManager.getHistory(actualConversationId); // Send to OpenAI - use the full modelName for the API call const response = await client.chat(modelConfig.modelName, history, { reasoning, provider: modelConfig.provider, }); // Add assistant response to conversation conversationManager.addMessage(actualConversationId, "assistant", response.content); logger.info("Chat completed", { conversationId: actualConversationId, modelId: actualModelId, }); return { content: [ { type: "text" as const, text: JSON.stringify({ conversationId: actualConversationId, response: response.content, reasoning: response.reasoning, modelId: actualModelId, }), }, ], }; } catch (error) { const errorMessage = error instanceof Error ? error.message : String(error); logger.error("Chat tool error", error instanceof Error ? error : new Error(errorMessage)); return { content: [ { type: "text" as const, text: `Error: ${errorMessage}`, }, ], }; } }
• src/mcp-tools.ts:21-41 (schema)

  Zod input schema for the 'chat' tool defining parameters: message (required), conversationId (optional), modelId (optional), reasoning (optional).
  inputSchema: z.object({ message: z.string().describe("The question or request to send—be clear and specific."), conversationId: z .string() .optional() .describe( "ID of the conversation to continue; omit to start a new one. Use the conversationId from prior responses to keep discussing the same topic." ), modelId: z .string() .optional() .describe( "ID of model to use (call list_models); omitted = default model. To switch models, pass a different modelId with your conversationId — you'll get a new conversationId with the conversation cloned to the new model." ), reasoning: z .boolean() .optional() .describe( "Set true to have the model show its reasoning steps, useful for complex problems." ), }),
• src/mcp-tools.ts:14-155 (registration)

  The complete registration of the 'chat' tool using server.registerTool, including the tool name, title, description, input schema, and handler function.
  // Chat tool server.registerTool( "chat", { title: "Chat with Another LLM Model", description: 'Send a message to an available LLM for help, second opinions, or brainstorming; start new conversations, continue existing ones, or switch models mid-chat. In the first message you shall provide as much context as possible, since the model has no idea of the problem.\n\nExample workflow:\n1. chat(message: "hello", modelId: "gpt-5-mini") → conversationId: "abc1"\n2. chat(message: "follow-up", conversationId: "abc1") → conversationId: "abc1" (continues)\n3. chat(message: "same question", conversationId: "abc1", modelId: "deepseek-r1") → conversationId: "xyz9" (cloned with new model)', inputSchema: z.object({ message: z.string().describe("The question or request to send—be clear and specific."), conversationId: z .string() .optional() .describe( "ID of the conversation to continue; omit to start a new one. Use the conversationId from prior responses to keep discussing the same topic." ), modelId: z .string() .optional() .describe( "ID of model to use (call list_models); omitted = default model. To switch models, pass a different modelId with your conversationId — you'll get a new conversationId with the conversation cloned to the new model." ), reasoning: z .boolean() .optional() .describe( "Set true to have the model show its reasoning steps, useful for complex problems." ), }), }, async ({ message, conversationId, modelId, reasoning }) => { try { logger.debug("Chat tool called", { hasConversationId: !!conversationId, modelId, }); let actualConversationId: string; let actualModelId: string; // Determine conversation ID and model ID if (conversationId) { const existing = conversationManager.getConversation(conversationId); if (!existing) { return { content: [ { type: "text" as const, text: `Error: Conversation not found: ${conversationId}`, }, ], }; } if (modelId && modelId !== existing.modelId) { // Clone conversation with new model actualConversationId = conversationManager.cloneConversation(conversationId, modelId); actualModelId = modelId; } else { // Continue existing conversation actualConversationId = conversationId; actualModelId = existing.modelId; } } else { // Create new conversation actualModelId = modelId || config.models[0].id; actualConversationId = conversationManager.createConversation(actualModelId); } // Validate model exists and get its full model name const client = openaiClients.get(actualModelId); if (!client) { return { content: [ { type: "text" as const, text: `Error: Model not configured: ${actualModelId}`, }, ], }; } // Get the full model name from config for API calls const modelConfig = config.models.find((m) => m.id === actualModelId); if (!modelConfig) { return { content: [ { type: "text" as const, text: `Error: Model configuration not found: ${actualModelId}`, }, ], }; } // Add user message to conversation conversationManager.addMessage(actualConversationId, "user", message); // Get conversation history const history = conversationManager.getHistory(actualConversationId); // Send to OpenAI - use the full modelName for the API call const response = await client.chat(modelConfig.modelName, history, { reasoning, provider: modelConfig.provider, }); // Add assistant response to conversation conversationManager.addMessage(actualConversationId, "assistant", response.content); logger.info("Chat completed", { conversationId: actualConversationId, modelId: actualModelId, }); return { content: [ { type: "text" as const, text: JSON.stringify({ conversationId: actualConversationId, response: response.content, reasoning: response.reasoning, modelId: actualModelId, }), }, ], }; } catch (error) { const errorMessage = error instanceof Error ? error.message : String(error); logger.error("Chat tool error", error instanceof Error ? error : new Error(errorMessage)); return { content: [ { type: "text" as const, text: `Error: ${errorMessage}`, }, ], }; } } );
• src/openai-client.ts:80-142 (helper)

  OpenAIClient.chat method called by the 'chat' tool handler to perform the actual LLM chat completion request via OpenAI API (or compatible). Handles message formatting, reasoning params, response extraction.
  async chat( modelId: string, messages: ChatMessage[], options?: ChatOptions ): Promise<{ content: string; reasoning?: string }> { try { logger.debug("Sending chat request to OpenAI", { model: modelId, messageCount: messages.length, reasoning: options?.reasoning, }); const openaiMessages = messages.map((msg) => ({ role: msg.role as "user" | "assistant", content: msg.content, })); const client = this.getClient(); // Build provider-specific reasoning params const reasoningParams = this.buildReasoningParams( options?.provider, options?.reasoning ?? false ); const response = await client.chat.completions.create({ model: modelId, messages: openaiMessages, ...reasoningParams, }); const textContent = response.choices[0]?.message?.content; if (!textContent) { throw new Error("No content in response from OpenAI"); } logger.debug("Received response from OpenAI", { model: modelId, tokens: response.usage?.total_tokens, }); // Extract reasoning if present and requested let reasoning: string | undefined; if (options?.reasoning) { reasoning = this.extractReasoning( response.choices[0].message as unknown as Record<string, unknown> ); } return { content: textContent, reasoning, }; } catch (error) { const errorMessage = error instanceof Error ? error.message : String(error); logger.error("OpenAI API error", { model: modelId, error: errorMessage, }); throw new Error(`Failed to get response from OpenAI: ${errorMessage}`); } }