How to Use the Numbers API MCP in Vercel AI SDK

Stream real-time numerical trivia and math facts directly into your Vercel AI SDK frontends for a responsive user experience.

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Connect Numbers API MCP to Vercel AI SDK

Create your Vinkius account to connect Numbers API 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 Numbers API with Vercel AI SDK

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Perplexity

Real-time math data streaming

Feed your UI with live mathematical properties using `get_math_fact`. Your users see the data flow instantly as the AI processes the request, keeping the interface snappy and alive. Because this MCP server works with the Vercel AI SDK, you avoid those annoying loading spinners. The tool output hits your stream and renders immediately in your React or Next.js components.

Historical context via MCP Server

Inject historical depth into your application using `get_year_fact` and `get_date_fact`. It's the easiest way to give your AI client a sense of time without managing a database yourself. Your agent simply queries the tool, and the result becomes part of the conversation history. It’s perfect for building dashboards that track events or educational tools that need quick, verified historical markers.

Dynamic trivia injection

Keep your app content fresh by pulling in random data with `get_random_fact` or specific number trivia via `get_trivia_fact`. It adds a layer of unexpected detail to any chatbot or interactive form you build. Since these tools integrate natively, you don't write extra fetch logic. Just point your AI client at the endpoint, and let the tools handle the data retrieval in the background.

Setup guide

Set up Numbers API 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 Numbers API 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 Numbers API 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 Numbers API. 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 Numbers API MCP in Vercel AI SDK

You use the @ai-sdk/mcp package to link the server to your runtime. Simply initialize the client with your endpoint URL, and the tools become available for your generative functions.

Yes, you can register all five tools at once. The SDK manages tool calling, so your agent can pick the right one based on the user's prompt.

It does. Because the communication is light and standard, it fits within the constraints of edge environments perfectly.

The server only touches public trivia and mathematical data. No user-specific information is ever sent to or processed by the external API endpoints.

Your agent will receive an error response from the tool. You should handle this in your code to show a fallback message to the user.

Use it with your favorite AI tools

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