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How to Use the WebScrapingAPI MCP in Mastra AI

Build robust multi-step data pipelines for Mastra AI that never fail.

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Connect WebScrapingAPI MCP to Mastra AI

Create your Vinkius account to connect WebScrapingAPI 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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Reliable E-commerce Data Acquisition

You can use `scrape_ecommerce_product` to get structured JSON containing price, title, and reviews from major retail sites. If the initial scrape fails due to a temporary block, Mastra AI's workflow engine automatically retries the operation. This reliable execution guarantees that your multi-step process doesn't halt over flaky web data. It keeps trying until the structured product information is successfully pulled.

High-Anonymity Scraping with Retries

When scraping sensitive or protected sites, use `scrape_via_residential_proxy`. If a single proxy fails or returns an error code, the workflow engine automatically switches to another residential IP address for the retry. This built-in failover mechanism is crucial. It ensures that your critical data gathering steps—like running `custom_api_scrape`—succeed without manual intervention.

Handling Complex JS Rendering Failures

If a target page requires JavaScript to fully render, use `scrape_js_rendered`. If the initial rendering attempt times out or fails, Mastra AI can trigger a backoff and retry with different parameters. This makes your workflow robust. It handles both simple static scrapes using `scrape_static_html` and complex JS renders, ensuring data integrity across multiple steps.

Setup guide

Set up WebScrapingAPI 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. 1

    Install dependencies

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

  2. 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. 3

    Discover and inject tools

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

  4. 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: "webscrapingapi-mcp-client",
  servers: {
    "webscrapingapi-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by WebScrapingAPI. 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 WebScrapingAPI MCP in Mastra AI

Mastra AI's workflow engine provides automatic retries and exponential backoff. If `scrape_and_auto_extract` fails on a certain site, the system doesn't quit; it waits and tries again using better credentials or proxies.
Yes. The workflow can be designed to attempt scraping first via a standard IP, and if that fails, automatically escalate the retry using `scrape_via_residential_proxy`. This layered approach makes your data collection resilient.
Mastra AI can manage multi-step searches. For instance, it runs `search_google_serp` to get initial leads, and if those fail, it gracefully switches the workflow to run `search_bing_serp` instead.
It's built for it. The MCP Server supports asynchronous tool calls that fit perfectly into Mastra AI's conditional branching and retry logic, making your entire process dependable.
It manages public web content, including structured search results (SERP), raw HTML, e-commerce product metadata, and general scraped text. The focus is entirely on external web sources.

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