Vercel AI SDK

npm install ai @modelcontextprotocol/sdk
npm install openai  # or your preferred provider

import {
  experimental_createMCPClient as createMCPClient,
  streamText,
} from 'ai';
import { openai } from '@ai-sdk/openai';
import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp';

async function main() {
  try {
    // Define your MCP server endpoint
    // Replace {your_client_id} with your actual client ID from the Observee dashboard
    const url = new URL('https://mcp.observee.ai/mcp?client_id={your_client_id}');

    // Create the MCP client with authentication
    const mcpClient = await createMCPClient({
      transport: new StreamableHTTPClientTransport(url, {
        requestInit: {
          headers: {
            'Authorization': 'Bearer {observee_api_key}' // Replace with your API key
          }
        }
      })
    });

    // Discover available tools from the MCP server
    const tools = await mcpClient.tools();
    console.log(`Connected to MCP server with ${Object.keys(tools).length} tools available`);

    // Use the tools in a streaming LLM call
    const result = await streamText({
      model: openai('gpt-4-turbo'),
      tools, // Pass the discovered tools to streamText
      prompt: 'Help me analyze the latest system metrics and provide insights',
      maxTokens: 1000,
    });

    // Handle the streaming response
    for await (const textPart of result.textStream) {
      process.stdout.write(textPart);
    }

    // Always close the client when done
    await mcpClient.close();
    
  } catch (error) {
    console.error('MCP Integration failed:', error);
    
    // Common error scenarios:
    if (error.message.includes('404')) {
      console.log('Check your client ID and ensure the MCP server is deployed');
    } else if (error.message.includes('authentication')) {
      console.log('Verify your API key and authentication configuration');
    }
  }
}

main().catch(console.error);