How to Use the TrueFoundry MCP in Vercel AI SDK

Q: Can I list available models with TrueFoundry and Vercel AI SDK?

Yes. Use truefoundrylistgatewaymodels to get a list of all foundation models accessible from the TrueFoundry unified AI gateway, letting you build client-side model selection UIs.

Q: Does TrueFoundry support OAuth authentication with Vercel AI SDK?

The Vercel AI SDK supports an authProvider for OAuth, which means you can secure your connections to the MCP Server endpoint using standard OAuth flows.

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Connect TrueFoundry MCP to Vercel AI SDK

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

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Run Chat Queries Directly in Your App

You call `truefoundry_run_gateway_chat` to push a model query string through TrueFoundry. This keeps your chat logic off the server and running directly against the unified gateway. Your AI client streams responses straight into React, Next.js, or Svelte components. The user sees the text appearing live, without waiting for a loading spinner.

Manage Backend Deployments Natively

Need to deploy a new backend container? Use `truefoundry_deploy_mcp_server` via your AI client. This spawns the logical process using TrueFoundry's service mesh. After deployment, check status or list running topologies by calling `truefoundry_get_deployment_status` or `truefoundry_list_deployments`. It keeps the whole lifecycle visible in your frontend.

Generate Semantic Vectors on Demand

Calculating embeddings is easy. Just call `truefoundry_generate_embeddings` to get secure semantic vectors using the unified abstraction. This allows you to build advanced search features right into your client-side product logic, without needing a separate backend service just for vector storage.

Setup guide

Set up TrueFoundry 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 TrueFoundry 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 TrueFoundry transactions",
});

console.log(text);
await mcpClient.close();

Get your connection token →

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Common questions about TrueFoundry MCP in Vercel AI SDK

You pass the MCP endpoint URL directly into your client setup. The SDK handles streaming the results from the TrueFoundry gateway, ensuring the user sees data in real-time.

Yes. Use `truefoundry_list_gateway_models` to get a list of all foundation models accessible from the TrueFoundry unified AI gateway, letting you build client-side model selection UIs.

This server handles conversation state and metadata. Specifically, it manages the JSON structure defining tool schemas via `truefoundry_get_mcp_server_info`.

The Vercel AI SDK supports an `authProvider` for OAuth, which means you can secure your connections to the MCP Server endpoint using standard OAuth flows.

Because of its design, the Vercel AI SDK is compatible with Edge Function environments. This lets you run your entire AI interaction stack close to your user base.

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