import { createMCPClient, ElicitationRequestSchema } from '@ai-sdk/mcp';
import { generateText } from 'ai';

// Create the MCP client with elicitation capability enabled
const mcpClient = await createMCPClient({
  transport: {
    type: 'sse',
    url: 'http://localhost:8083/sse',
  },
  capabilities: {
    elicitation: {},
  },
});

// Register a handler for elicitation requests
mcpClient.onElicitationRequest(ElicitationRequestSchema, async request => {
  console.log('Server is requesting:', request.params.message);
  console.log('Expected schema:', request.params.requestedSchema);

  // Collect user input according to the schema
  // This is where you would implement your own logic to prompt the user
  const userData = await promptUserForInput(request.params.requestedSchema);

  // Return the result with one of three actions:
  // - 'accept': User provided the requested information
  // - 'decline': User chose not to provide the information
  // - 'cancel': User cancelled the operation entirely
  return {
    action: 'accept',
    content: userData,
  };
});

try {
  const tools = await mcpClient.tools();

  const { text } = await generateText({
    model: 'openai/gpt-4o-mini',
    tools,
    prompt: 'Register a new user account',
  });

  console.log('Response:', text);
} finally {
  await mcpClient.close();
}

// Example implementation of promptUserForInput
async function promptUserForInput(
  schema: unknown,
): Promise<Record<string, unknown>> {
  // Implement your own logic to collect input based on the schema
  // This could be:
  // - A CLI prompt using readline
  // - A web form
  // - A GUI dialog
  // - Any other input mechanism

  // For this example, we'll return mock data
  return {
    username: 'johndoe',
    email: '[email protected]',
    password: 'securepassword123',
    newsletter: true,
  };
}