How to Use the Exa AI MCP in Mastra AI

Q: Does Exa AI support human-in-the-loop with Mastra AI?

You can enable requireToolApproval for any search tool. This forces the agent to pause until you confirm the search query is correct.

Build resilient search workflows with the Mastra AI framework and this MCP Server.

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Connect Exa AI MCP to Mastra AI

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

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Claude

Perplexity

Automated search workflows

Chain `search_web` and `get_contents` into your agent's workflow engine to build automated research loops. If a search yields nothing, you can trigger a retry or a different query strategy automatically. This handles the complexity of gathering data without manual intervention. It is ideal for agents that need to perform multi-step research tasks.

Conditional search branching

Use the output of `get_search_links` to decide which path your agent takes next. If the links are low quality, route the workflow to a different search parameter. This gives your agents the ability to reason about their search strategy. It prevents them from hitting dead ends and keeps your operations moving.

Reliable multi-step data gathering

Combine `find_similar` and `search_with_contents` to build agents that map out specific topics. The framework manages the state and retries if a network request hangs. This keeps your data gathering stable. You get a consistent stream of information for your agent to analyze and act upon.

Setup guide

Set up Exa AI 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 AI 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-ai-mcp-client",
  servers: {
    "exa-ai-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Common questions about Exa AI MCP in Mastra AI

Register the server in your MCPClient configuration and spread the tools into your agent's tool set. Then, include the tool names in your agent's instructions.

Yes. You can configure your agent's workflow to monitor for failures and re-run the `search_web` tool with updated parameters.

You can enable `requireToolApproval` for any search tool. This forces the agent to pause until you confirm the search query is correct.

The server treats each request as an isolated event. Your search history and retrieved content are discarded as soon as the agent completes its task.

The server provides raw text content. If the data exceeds your memory limits, you can use the agent's logic to process the text in smaller chunks.

Use it with your favorite AI tools

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