/**
* AI SDK Agent Example - Remote (dbt Cloud)
*
* Demonstrates connecting to dbt-mcp via dbt Cloud's hosted MCP endpoint.
* Uses Vercel's AI SDK to wire up an LLM with dbt-mcp tools.
*/
import { experimental_createMCPClient as createMCPClient, streamText } from "ai";
import { openai } from "@ai-sdk/openai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";
import * as readline from "readline";
const host = process.env.DBT_HOST ?? "cloud.getdbt.com";
const token = process.env.DBT_TOKEN;
const prodEnvId = process.env.DBT_PROD_ENV_ID;
const accountPrefix = process.env.MULTICELL_ACCOUNT_PREFIX;
if (!token || !prodEnvId) {
console.error("Missing required environment variables: DBT_TOKEN, DBT_PROD_ENV_ID");
process.exit(1);
}
async function main() {
// Connect to dbt Cloud's hosted MCP server
const baseHost = accountPrefix ? `${accountPrefix}.${host}` : host;
const transport = new StreamableHTTPClientTransport(
new URL(`https://${baseHost}/api/ai/v1/mcp/`),
{
requestInit: {
headers: {
Authorization: `token ${token}`,
"x-dbt-prod-environment-id": prodEnvId!,
},
},
}
);
// Initialize MCP client and fetch available tools
const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();
console.log("Connected to dbt MCP server");
console.log(`Available tools: ${Object.keys(tools).length}`);
console.log("Type 'quit' to exit\n");
const rl = readline.createInterface({
input: process.stdin,
output: process.stdout,
});
const prompt = (query: string): Promise<string> =>
new Promise((resolve) => rl.question(query, resolve));
try {
// Main chat loop
while (true) {
const userInput = await prompt("You: ");
if (userInput.toLowerCase() === "quit") {
break;
}
if (!userInput.trim()) {
continue;
}
process.stdout.write("Assistant: ");
// Stream response from LLM, allowing it to call dbt-mcp tools
const result = streamText({
model: openai("gpt-4o"),
tools,
maxSteps: 10,
system: "You are a helpful assistant with access to dbt Cloud tools. Use them to answer questions about the user's dbt project, metrics, models, and data.",
prompt: userInput,
onStepFinish: ({ toolCalls, toolResults }) => {
if (toolCalls?.length) {
for (const call of toolCalls) {
console.log(`\n[Tool: ${call.toolName}]`);
}
}
if (toolResults?.length) {
for (const result of toolResults) {
const preview = JSON.stringify(result.result).slice(0, 200);
console.log(`[Result: ${preview}${preview.length >= 200 ? "..." : ""}]`);
}
process.stdout.write("\nAssistant: ");
}
},
});
try {
for await (const chunk of (await result).textStream) {
process.stdout.write(chunk);
}
} catch (err) {
console.error(`\n[Error: ${err instanceof Error ? err.message : err}]`);
}
console.log("\n");
}
} finally {
rl.close();
await mcpClient.close();
console.log("Goodbye!");
}
}
main().catch(console.error);
