How to Use the Togai (Usage Metering & Pricing) MCP in Vercel AI SDK

Q: Does Togai (Usage Metering & Pricing) require specific setup for Vercel AI SDK?

You use npm install ai @ai-sdk/mcp and then call the MCP client. Remember to always wrap up by calling mcpClient.close() when you're done with the session.

Real-time Billing Data Streaming with Vercel AI SDK

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Connect Togai (Usage Metering & Pricing) MCP to Vercel AI SDK

Create your Vinkius account to connect Togai (Usage Metering & Pricing) 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 Togai (Usage Metering & Pricing) with Vercel AI SDK

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Check Entitlements in Real Time with the MCP Server

The `check_entitlement` tool lets you verify if a user has access to a feature before calling an API. Since this data streams right into your React or Next.js frontend, the user sees the entitlement check result pop up live, without hitting a loading spinner. This is clutch for building user-facing products. You call `mcpClient.tools()` and pass it through `streamText`, so when the AI agent hits `check_entitlement`, the data appears instantly in the UI.

Process Usage Events with Vercel AI SDK

When a user performs an action, you can use `ingest_event` to send that usage record. The event needs a schema name, timestamp, account ID, and attributes—the basics for any good metering system. This data feeds directly into your billing logic. Your AI agent sends the event using the SDK's streaming capabilities, making the entire process feel immediate to the end user.

Manage Customer Accounts with Vercel AI SDK

Need to create a new paying customer? The `create_customer` tool handles that. You pass the necessary details—name, email, etc.—and your agent gets a confirmation back immediately. This lets you build self-service portals where the user interacts with the billing system directly through the AI stream. It's all visible and real time.

Setup guide

Set up Togai (Usage Metering & Pricing) 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 Togai (Usage Metering & Pricing) 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 Togai (Usage Metering & Pricing) 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 Togai. 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 Togai (Usage Metering & Pricing) MCP in Vercel AI SDK

You call the MCP Server tools directly from your client-side code. The agent sends a request like `list_invoices`, and that result streams back to your front end instantly, letting the user see the invoice data appearing as it arrives.

This server handles billing and usage event data. Specifically, it touches account IDs, customer records, and raw usage events. Always ensure your authProvider validates who's calling these tools.

Since the results stream live into your UI, error messages are also streamed. If an attempt to `get_wallet_balance` fails, the user sees that failure message in real time, making debugging and UX much clearer.

Yes. Because it streams results directly into your UI, the system handles high data throughput without forcing users to wait for a single large API response.

You use `npm install ai @ai-sdk/mcp` and then call the MCP client. Remember to always wrap up by calling `mcpClient.close()` when you're done with the session.

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