How to Use the Browserless (Playwright Cloud) MCP in Mastra AI

Q: Is Browserless (Playwright Cloud) compatible with Mastra AI's human-in-the-loop?

Absolutely, you can enable requireToolApproval for any browser task. This forces the agent to pause until you confirm the scraping action is safe to run.

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Connect Browserless (Playwright Cloud) MCP to Mastra AI

Create your Vinkius account to connect Browserless (Playwright Cloud) 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 Browserless (Playwright Cloud) with Mastra AI

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Add browser automation to your Mastra AI workflows

The `run_custom_function` tool lets your agents perform multi-step interactions like filling out forms or navigating deep menus. You define the logic, and the agent executes the sequence remotely. If a step fails, your Mastra workflow engine catches the error. You can configure automatic retries or branch to a different logic path based on the browser response.

Scrape behind proxies with Mastra AI

Your agent calls `scrape_with_proxy` to route requests through specific endpoints. This is critical for tasks requiring geo-located data that your standard server IP cannot reach. It plugs directly into your agent tools. You maintain full control over the network path while the MCP Server manages the underlying browser instance.

Wait for elements before your Mastra AI agent proceeds

Use `scrape_with_wait` to ensure specific CSS selectors exist before the agent attempts extraction. This prevents your workflows from failing on pages that load content dynamically. The tool pauses execution until the page state matches your requirements. Your agent then consumes the clean data without needing extra sleep timers.

Setup guide

Set up Browserless (Playwright Cloud) 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 Browserless (Playwright Cloud) 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: "browserless-playwright-cloud-mcp-client",
  servers: {
    "browserless-playwright-cloud-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Common questions about Browserless (Playwright Cloud) MCP in Mastra AI

You import the MCP client into your agent definition and spread the tool list into your agent's tool array. Mastra then handles the execution and any required tool approvals.

Yes, you can wrap the tool calls in your workflow logic. If the browser fails to return the expected data, the agent can trigger a retry with a fresh browser session.

Absolutely, you can enable `requireToolApproval` for any browser task. This forces the agent to pause until you confirm the scraping action is safe to run.

Your agent session remains isolated within the Vinkius sandbox. We treat your browsing history and extracted page content as transient data that gets purged after the task finishes.

It provides the raw data your workflow needs to make decisions. You can inspect the output of `get_html_content` and use conditional logic to dictate where the agent goes next.

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