How to Use the Marketaux MCP in Vercel AI SDK

Q: Can I stream Marketaux sentiment analysis directly into a React component using the Vercel AI SDK?

Yes, you can. By passing the tools from mcpClient.tools() into streamText, the Vercel AI SDK streams the raw JSON output from getsentimentanalysis directly to your frontend. This lets you render live sentiment charts while the model is still processing the news text.

Q: What is the fastest way to set up the Marketaux MCP Server with the Vercel AI SDK?

Run npm install ai @ai-sdk/mcp and initialize the client using createMCPClient with your Vinkius HTTP transport URL. You then spread mcpClient.tools() directly into your Vercel AI SDK generateText tools parameter to immediately access endpoints like getsymbols.

Q: How does the Vercel AI SDK handle multiple stock symbol queries?

The Vercel AI SDK uses getsymbols to resolve multiple tickers in a single request. It then passes those verified symbols to searchnews to get targeted financial coverage for the entire portfolio at once.

Stream live Marketaux sentiment scores and global market news directly into your Vercel AI SDK frontends without loading spinners.

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Connect Marketaux MCP to Vercel AI SDK

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Setup Marketaux with Vercel AI SDK

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Real-time Marketaux sentiment feeds for Vercel AI SDK.

Stop making users wait for heavy financial analysis to load. This MCP integration lets you stream real-time Marketaux sentiment scores directly into your Next.js or React components. By calling `get_sentiment_analysis`, your Vercel AI SDK client pulls active market mood metrics and renders them on the screen word-by-word, keeping your financial dashboard fast and responsive. You can pair this with `get_trending_news` to build live-updating Vercel AI SDK tickers that display momentum shifts as they happen. The SDK handles the streaming connection directly, meaning your app displays what is driving the market right now without complex polling logic.

Live status checks before executing user queries.

Before your Vercel AI SDK agent suggests a stock trade, it needs to know if the market is open. Your app calls `get_stock_market_status` to verify trading hours across global exchanges instantly. This prevents your LLM from generating stale trading advice when the New York or London desks are closed for the day. Users get immediate feedback because the tool execution happens at the edge. The Vercel AI SDK takes the output from `search_exchanges` and formats the active trading windows directly inside the chat interface, keeping your token costs low and your interface snappy.

Deep stock metadata search without interface lag.

Give your users the ability to drill down into specific Marketaux sectors. Your Vercel AI SDK interface uses `get_entities` and `list_industries` to map raw company names to their exact market tickers and sectors. The SDK handles these tool calls in the background, updating the UI state dynamically as the user types. You can also pull complete indices lists using `get_indices` to show macro-level performance in your Vercel AI SDK layout. Because this runs on Vinkius, the data arrives pre-formatted for your streaming templates, bypassing the need for manual API response parsing.

Setup guide

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

You manage this by configuring the Vinkius MCP Server endpoint, which acts as a buffer. The Vercel AI SDK calls `get_latest_news` through our managed gateway, handling authentication and connection pooling automatically so your edge functions do not exhaust your API limits.

Yes, you can. By passing the tools from `mcpClient.tools()` into `streamText`, the Vercel AI SDK streams the raw JSON output from `get_sentiment_analysis` directly to your frontend. This lets you render live sentiment charts while the model is still processing the news text.

Run `npm install ai @ai-sdk/mcp` and initialize the client using `createMCPClient` with your Vinkius HTTP transport URL. You then spread `mcpClient.tools()` directly into your Vercel AI SDK `generateText` tools parameter to immediately access endpoints like `get_symbols`.

The Vercel AI SDK uses `get_symbols` to resolve multiple tickers in a single request. It then passes those verified symbols to `search_news` to get targeted financial coverage for the entire portfolio at once.

All search terms, tickers, and financial news queries are processed within an isolated V8 sandbox on Vinkius. This zero-trust architecture ensures that your proprietary watchlists never persist on disk, executing each request in an ephemeral environment that is destroyed the moment the session ends.

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