How to Use the Front MCP in Vercel AI SDK

Q: How do I connect the Front MCP Server to my Vercel AI SDK project?

Install @ai-sdk/mcp and ai. Initialize the connection using createMCPClient with your HTTP endpoint URL, and pass the tools directly into streamText.

Q: Can I stream active customer threads using Front and Vercel AI SDK?

Yes. Use the listconversationmessages tool inside your model's tool call configuration to stream the full history of any conversation directly into your application's UI.

Q: Can I update ticket statuses directly from my Next.js UI?

Yes, you use updateconversationstatus to open, archive, or snooze threads. The tool maps directly to Vercel AI SDK's execution loop, applying changes instantly.

Q: How is my Front contact information secured in Vercel?

When your AI client requests contact records via getcontactinfo, the Vinkius MCP Server handles the request inside an isolated V8 sandbox, meaning your raw API credentials are never exposed to the client-side browser.

Feed live Front team conversations directly into your React UI using the Vercel AI SDK.

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Connect Front MCP to Vercel AI SDK

Create your Vinkius account to connect Front 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 Front with Vercel AI SDK

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Stream Front Data to React via Vercel AI SDK

The `list_conversations` tool fetches your active team threads so your UI can display them without delay. Your web app calls this through the Vercel AI SDK MCP integration, letting users see incoming customer messages populate their screens in real-time. Using `list_shared_inboxes` alongside this setup gives your users immediate visibility into which team queues are active. It bypasses slow REST polling, rendering clean lists directly in your Next.js components.

Send and Track Replies Live

The `reply_to_conversation` tool sends messages back to customers directly from your custom AI interface. By binding this operation to your Vercel AI SDK MCP client, users watch the status update as the message goes out. You can immediately chain `get_message_content` to verify the exact text that was sent. This keeps your custom agent dashboard accurate, showing the raw message payload in the UI the second the API responds.

Query Front Conversations via MCP Server

The `search_conversations_by_query` tool scans your team's entire message history based on specific user inputs. Running this on Vercel Edge Functions means your AI client filters support tickets instantly without hitting heavy backend bottlenecks. Combine this with `get_conversation_details` to pull metadata like tags or assignees. Your interface displays the full context of the customer's history before the user even clicks the thread.

Setup guide

Set up Front 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 Front 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 Front 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 Front. 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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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

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Common questions about Front MCP in Vercel AI SDK

Install `@ai-sdk/mcp` and `ai`. Initialize the connection using `createMCPClient` with your HTTP endpoint URL, and pass the tools directly into `streamText`.

Yes. Use the `list_conversation_messages` tool inside your model's tool call configuration to stream the full history of any conversation directly into your application's UI.

The SDK executes calls as they are triggered by the model. To prevent hitting API ceilings during heavy `search_conversations_by_query` usage, implement a debounce layer or middleware before passing the tools to your model.

Yes, you use `update_conversation_status` to open, archive, or snooze threads. The tool maps directly to Vercel AI SDK's execution loop, applying changes instantly.

When your AI client requests contact records via `get_contact_info`, the Vinkius MCP Server handles the request inside an isolated V8 sandbox, meaning your raw API credentials are never exposed to the client-side browser.

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