How to Use the Fibery MCP in Vercel AI SDK

Q: How do I query Fibery data within Vercel AI SDK?

Use the queryentities tool within your generateText call. You pass your schema details to the agent, which returns the requested entities directly to your stream.

Q: Can I update Fibery records from Vercel AI SDK components?

Yes, you connect the updateentity tool to your UI event handlers. The agent executes the request and you can immediately trigger a re-fetch to update the view.

Q: Does this support complex workspace schemas?

Absolutely. The getschema tool provides a full map of your apps and fields, allowing the agent to navigate nested structures without manual configuration.

Stream Fibery work items directly into your Vercel AI SDK interface for real-time, zero-latency data updates.

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Connect Fibery MCP to Vercel AI SDK

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

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Real-time Entity Management

Feed live workspace data into your frontend using `query_entities` and `get_entity`. Your users watch data populate instantly as the agent pulls records from your database. This MCP Server handles the handshake so your components stay responsive. You stop waiting for spinners and start showing results the second `get_schema` maps your workspace.

Interactive Task Updates

Trigger state changes in your app by mapping `update_entity` to UI actions. When a user clicks a button, the agent fires the update directly to your workspace backend. It keeps your interface in sync with reality. You verify the change by calling `get_entity` again to confirm the data reflects the new status.

Integrated Comment Threads

Pull discussions into your dashboard with `get_comments` and push replies using `add_comment`. Your UI displays the conversation flow without leaving the page. This keeps the context centered where the work happens. You map these tools to your comment components to build a live-updating discussion feed.

Setup guide

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

Use the `query_entities` tool within your `generateText` call. You pass your schema details to the agent, which returns the requested entities directly to your stream.

Yes, you connect the `update_entity` tool to your UI event handlers. The agent executes the request and you can immediately trigger a re-fetch to update the view.

We handle authentication via a single endpoint token. Your data stays within the encrypted transport layer, and the agent only touches the specific entities you authorize.

Absolutely. The `get_schema` tool provides a full map of your apps and fields, allowing the agent to navigate nested structures without manual configuration.

The server returns an error object that you can catch in your stream handler. You should implement basic retry logic to ensure your UI state matches the workspace.

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