How to Use the Feathery MCP in Vercel AI SDK

Q: How do I configure the Feathery MCP Server inside a Vercel AI SDK project?

Install @ai-sdk/mcp and set up an HTTP transport for the MCP client pointing to your Vinkius endpoint. Pass the tools directly into generateText or streamText and remember to call close() when the execution finishes.

Q: Can I stream live form progress using Feathery and Vercel AI SDK?

Yes, you use getformsession to grab the current state of a user's form. The SDK streams this state data directly to your frontend components so you can render progress bars or partial answers live.

Q: Do I need to manage state when fetching workflows?

No, because getworkflowdetails returns the static configuration and current state of a workflow directly. Your agent reads this JSON payload and decides the next UI state to render.

Pipe real-time user form sessions and workflow states directly into your Next.js UI using Feathery tools with the Vercel AI SDK.

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Connect Feathery MCP to Vercel AI SDK

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

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Active Form Tracking via the Feathery MCP Server

The `get_form_session` tool pulls active form states to let your frontend render live progress as users type. You feed this tool directly into your Vercel AI SDK stream to update UI components without making the user wait for a page reload. Your application calls `get_form_details` via our MCP Server to verify form fields on the fly. This prevents broken submissions by matching active layouts with user inputs before they hit your database.

Instant User Data Resolution

The `get_user_data` tool fetches all submitted field values across multiple forms for any single user ID. Passing this tool to `streamText` allows your agent to render personalized dashboard cards as the response streams. You also use `list_users` to populate autocomplete fields in your admin panel. The Vercel AI SDK handles these MCP tool calls at the edge, keeping latency low.

Real-Time Integration Auditing

The `list_connector_logs` tool exposes recent API connector error logs for any form you target. When debugging webhook failures, your AI client pulls these logs and streams the exact error payload straight to your terminal or developer console. To make this work, you pull environment contexts using `list_environments` to isolate production bugs from staging ones. You get clean, actionable error data without digging through cloud watch logs.

Setup guide

Set up Feathery 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 Feathery 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 Feathery 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 Feathery. 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 Feathery MCP in Vercel AI SDK

Install `@ai-sdk/mcp` and set up an HTTP transport for the MCP client pointing to your Vinkius endpoint. Pass the tools directly into `generateText` or `streamText` and remember to call `close()` when the execution finishes.

Yes, you use `get_form_session` to grab the current state of a user's form. The SDK streams this state data directly to your frontend components so you can render progress bars or partial answers live.

The SDK relies on your Vinkius connection to manage the raw requests. If you exceed limits while calling `list_forms` or `get_user_data`, the client throws standard HTTP errors that you handle in your application's catch block.

No, because `get_workflow_details` returns the static configuration and current state of a workflow directly. Your agent reads this JSON payload and decides the next UI state to render.

All user-submitted fields and account configurations fetched via this MCP Server pass through an ephemeral, zero-trust V8 sandbox. Vinkius never writes this sensitive form data to persistent storage, keeping your user inputs isolated and secure.

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