How to Use the Exa AI MCP in Vercel AI SDK

Q: How do I stream Exa AI search results using the Vercel AI SDK?

Install @ai-sdk/mcp and set up an HTTP client pointing to your Vinkius MCP endpoint. Pass the tools from your client directly into streamText to let your agent call semanticsearch and stream the output live.

Q: Does the Vercel AI SDK support automatic tool calling with Exa AI?

Absolutely. When you pass the MCP Server tools to generateText or streamText, the SDK automatically decides when to trigger tools like searchwithcontents based on the user's prompt.

Q: How do I handle connection timeouts during deep web crawls?

Use performlivecrawl only when you need fresh data from a live site. For standard queries, stick to semanticsearch which returns immediately, or check the endpoint health first using getapistatus.

Pipe neural search results directly into your React or Next.js UI using Vercel AI SDK and Exa AI with zero loading lag.

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Connect Exa AI MCP to Vercel AI SDK

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

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Real-time UI streaming with Vercel AI SDK

Standard search engines make your users stare at spinner icons while waiting for a complete payload. By pairing this MCP Server with your frontend, your agent can execute a `semantic_search` and immediately pipe the neural matches right into your streaming text components. You can feed the output of `get_query_highlights` directly into a React component as the chunks arrive. This means your users read relevant snippets instantly instead of waiting for a heavy API response to finish resolving on the edge.

Smart web extraction on Edge Functions

Vercel's edge network demands lightweight execution, which is why this integration avoids bloated headless browser dependencies. When your user drops a URL, your agent runs `extract_page_content` over a clean HTTP transport to pull clean text without crashing your edge runtime. If you need to verify if the external API is responding before firing a heavy user query, just run a quick `get_api_status` check. This keeps your edge functions fast, predictable, and cheap by preventing unnecessary execution timeouts.

Narrowing search context dynamically in the client

Frontend agents perform best when they aren't drowned in noisy web data. Use this MCP Server inside your streaming route to lock your agent's search space to trusted documentation sites or developer portals with `search_specific_domains`. If your user wants to find related resources, your UI can trigger `find_similar_pages` based on their current page URL. This returns highly structured, relevant matches that render in your Next.js layout without any manual data formatting.

Setup guide

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

Install `@ai-sdk/mcp` and set up an HTTP client pointing to your Vinkius MCP endpoint. Pass the tools from your client directly into `streamText` to let your agent call `semantic_search` and stream the output live.

Yes. This MCP Server works perfectly on Vercel Edge Functions because it offloads the heavy neural processing to Exa, allowing your edge route to call `extract_page_content` or `keyword_search` over light HTTP connections.

Absolutely. When you pass the MCP Server tools to `generateText` or `streamText`, the SDK automatically decides when to trigger tools like `search_with_contents` based on the user's prompt.

Use `perform_live_crawl` only when you need fresh data from a live site. For standard queries, stick to `semantic_search` which returns immediately, or check the endpoint health first using `get_api_status`.

Your search queries, target URLs, and extracted page contents are sent directly to the Exa API via Vinkius's secure, ephemeral V8 sandboxes. No search terms or scraped text are stored or cached on our servers after the request completes.

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