How to Use the Exa MCP in Mastra AI

Build resilient search workflows in Mastra AI with automatic retries and conditional logic for your complex agent tasks.

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Connect Exa MCP to Mastra AI

Create your Vinkius account to connect Exa to Mastra AI 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 with Mastra AI

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Reliable search in Mastra AI workflows

Your Mastra AI agents can now handle web research as a standard step in any complex pipeline. Using `exa_search` allows the agent to decide when to gather data based on the current workflow state. If a search fails, the built-in Mastra retry logic catches it. You build stable, multi-step processes that don't fall over when the internet flickers.

Automated context gathering

Once your agent identifies a promising URL, `exa_get_contents` pulls the raw text for analysis. This step happens automatically as part of your defined agent chain. You avoid manual parsing errors by letting the tool handle the heavy lifting. Your agents spend less time cleaning data and more time acting on it.

Expanding research horizons

Use `exa_find_similar` to broaden the scope of your agent's research autonomously. It finds related sources that might be hidden from standard keyword tools. Your Mastra AI agents can now perform recursive discovery tasks. This adds a layer of depth to your automation that was previously impossible to script.

Setup guide

Set up Exa MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Exa tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "exa-mcp-client",
  servers: {
    "exa-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Exa Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Exa tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Exa transactions"
);
console.log(result.text);

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Common questions about Exa MCP in Mastra AI

Yes, you can use the tool results to trigger conditional logic in your workflows. If the search returns no results, your agent can branch to a different discovery strategy.

Mastra handles the execution, and the server provides clear error codes if a request fails. You can set up exponential backoff in your agent definition to manage this.

You can pass parameters like date ranges or categories directly into the tool arguments. This gives you precise control over the data your agents consume.

Vinkius hosts and manages the server for you. You just point your agent configuration to the provided endpoint URL.

The server only touches the specific web content you request via tool calls. It does not ingest or retain your internal business records or private user logs.

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