How to Use the Loop MCP in Vercel AI SDK

Q: Does Loop expose raw sentiment data to the Vercel AI SDK?

The server provides granular metrics through getsentimentmetrics. You receive the raw numbers, allowing your frontend to format them as needed.

Stream real-time NPS and CSAT data directly into your React or Next.js UI using the Loop MCP Server with the Vercel AI SDK.

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Connect Loop MCP to Vercel AI SDK

Create your Vinkius account to connect Loop to Vercel AI SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Loop with Vercel AI SDK

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Live feedback streaming in Vercel AI SDK

Pipe customer sentiment directly into your frontend components as it arrives. By using `list_feedback` inside your streamText calls, you bypass loading states and give your users immediate visibility into product health. Your interface renders feedback items the moment the AI retrieves them. This provides a fluid experience where data updates in real-time without refreshing the page.

Automated sentiment analysis

Calculate your current standing with `get_sentiment_metrics` to drive UI changes dynamically. You can trigger specific feedback forms or dashboard alerts based on the exact numbers returned by the server. This approach lets you adjust the user experience based on hard data. You stop guessing what the customer feels and start reacting to the actual metrics.

Contextual feedback management

Attach internal notes to specific records using `add_internal_note` while staying inside your application code. This keeps your team aligned without switching tabs to a separate dashboard. When you need to dig deeper, `get_feedback_details` pulls the full history for any entry. You build a tighter loop between user reports and your development process.

Setup guide

Set up Loop MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Loop tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Loop transactions",
});

console.log(text);
await mcpClient.close();

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Loop. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Loop MCP in Vercel AI SDK

Install the MCP client package via npm and initialize it with your server URL. Once configured, pass the tools into your generateText call to start querying data.

Yes, the transport layer is compatible with Edge Functions. Your application fetches and processes feedback metrics with minimal latency.

The server provides granular metrics through `get_sentiment_metrics`. You receive the raw numbers, allowing your frontend to format them as needed.

Vinkius handles all authentication via an endpoint token. Your AI client communicates over an encrypted tunnel, ensuring your feedback data stays private.

It touches feedback text, NPS scores, and CSAT ratings. The server limits access to these specific fields, preventing exposure of unrelated account information.

Use it with your favorite AI tools

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