How to Use the Alchemer MCP in Vercel AI SDK

Q: How do I stream Alchemer survey questions using the Vercel AI SDK?

You call mcpClient.tools() to get the listsurveyquestions tool and pass it to streamText. This lets your LLM fetch the question metadata and stream the structure directly into your React frontend. Always remember to call mcpClient.close() when the stream finishes.

Q: How does Vercel AI SDK handle campaign distribution lists?

Your LLM uses listsurveycampaigns to check active distributions directly within your chat UI. The SDK feeds this campaign data into the model context so it can display active outreach efforts to your team.

Build live-updating React dashboards that stream Alchemer survey data directly to your users with Vercel AI SDK.

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Connect Alchemer MCP to Vercel AI SDK

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Setup Alchemer with Vercel AI SDK

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Stream real-time survey lists to your Next.js frontend

The `list_surveys` tool lets your AI client pull your active Alchemer forms and feed them straight into the Vercel AI SDK MCP context. Instead of waiting for a slow Alchemer backend fetch to complete, your Next.js users watch their survey list render component-by-component on the page. React components update instantly as Alchemer feedback trickles into your Vercel AI SDK frontend. You can wire this up to render custom cards for each active Alchemer questionnaire without writing boilerplate Next.js state-management code.

Render Alchemer response details in live chat bubbles

The `get_response_details` tool fetches raw feedback submissions so your Vercel AI SDK agent can analyze them live during a chat session. When a support representative asks about a specific ticket, your Vercel AI SDK agent pulls the Alchemer feedback payload and streams the parsed summary directly into the conversation. This Alchemer setup runs cleanly inside Vercel Edge Functions to keep latency low. Your interface stays responsive because the text renders word-by-word as the SDK processes raw Alchemer feedback details.

Build interactive Alchemer report viewers with this MCP Server

The `list_survey_reports` tool exposes feedback summaries that your Vercel AI SDK UI can parse and display as interactive Next.js charts. By feeding these Alchemer reports into `streamText`, your application lets users ask natural language questions about their feedback trends and see visual charts draw in real time. You avoid the typical loading spinner pattern entirely when loading Alchemer reports. The raw report structure converts into clean Next.js elements on the fly, making complex feedback data instantly readable.

Setup guide

Set up Alchemer 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 Alchemer 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 Alchemer transactions",
});

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Alchemer. 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 Alchemer MCP in Vercel AI SDK

You call `mcpClient.tools()` to get the `list_survey_questions` tool and pass it to `streamText`. This lets your LLM fetch the question metadata and stream the structure directly into your React frontend. Always remember to call `mcpClient.close()` when the stream finishes.

Yes, the transport layer works perfectly in edge environments. When you call tools like `list_surveys` through the SDK, the connection runs over lightweight HTTP. This keeps your edge execution times low and your surveys loading fast.

Your LLM uses `list_survey_campaigns` to check active distributions directly within your chat UI. The SDK feeds this campaign data into the model context so it can display active outreach efforts to your team.

No, you do not need any custom endpoints. The Vercel AI SDK connects directly to the MCP Server endpoint, giving your model instant access to `list_survey_reports` with zero extra backend routing.

Your survey responses and contact lists are protected inside an isolated V8 sandbox on Vinkius. No customer feedback is ever stored or cached on the proxy, meaning your raw survey submissions stream securely through the MCP Server to your edge runtime.

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