Can the agent bypass Cloudflare screens when scraping?

Yes. If standard scraping (`scrape_html`) encounters a block, you can prompt the agent to 'Retry using premium proxies' (`scrape_premium`). This engages ScraperAPI's massive pool of residential IP addresses to mimic normal human traffic perfectly.

How can I extract pricing data directly from Amazon?

Instead of attempting to parse Amazon's chaotic HTML structure, just ask your agent: 'Get the Amazon product details for ASIN B0BQK...'. The agent uses `scrape_amazon` to return structured JSON data, neatly outputting the item's price and stock availability.

Does the scraping tool consume my LLM context window linearly?

Not if you use async jobs. For heavy pages, tell the agent 'Create an async job to scrape this URL'. It will return a Job ID (`create_async_job`), freeing up the chat. You can then ask the agent to 'check on the async job' (`get_async_job`) whenever you're ready.

ScraperAPI MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: ScraperAPI
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect ScraperAPI through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to ScraperAPI "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in ScraperAPI?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About ScraperAPI MCP Server

Connect your ScraperAPI account to any AI agent to bypass IP bans, CAPTCHAs, and complex anti-bot systems. Allow your agent to scrape the web dynamically using a pool of millions of proxies.

Pydantic AI validates every ScraperAPI tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Render JavaScript — Command your AI to fetch data from SPAs (Single Page Applications) like React or Vue sites flawlessly
Structured E-commerce & SEO — Extract parsed Amazon product pages via ASIN or pull Google SERP attributes in structured JSON formats directly to your chat
Premium Unblocking — Access high-quality residential proxies automatically when dealing with ultra-secure or aggressive Cloudflare-protected targets
Asynchronous & Visual Scraping — Spawn background scraping jobs for slow-loading pages or ask the AI to generate full-page screenshot URLs upon request

The ScraperAPI MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect ScraperAPI to Pydantic AI via MCP

Follow these steps to integrate the ScraperAPI MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from ScraperAPI with type-safe schemas

Why Use Pydantic AI with the ScraperAPI MCP Server

Pydantic AI provides unique advantages when paired with ScraperAPI through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your ScraperAPI integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your ScraperAPI connection logic from agent behavior for testable, maintainable code

ScraperAPI + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the ScraperAPI MCP Server delivers measurable value.

Type-safe data pipelines: query ScraperAPI with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple ScraperAPI tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query ScraperAPI and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock ScraperAPI responses and write comprehensive agent tests

ScraperAPI MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect ScraperAPI to Pydantic AI via MCP:

create_async_job

Returns a job ID. Creates an asynchronous scraping job

custom_scrape

Performs a scrape with custom ScraperAPI parameters

get_account_stats

Retrieves API usage statistics

get_async_job

Retrieves the status and result of an async job

get_screenshot_link

Generates a URL to capture a full-page screenshot

scrape_amazon

Retrieves structured Amazon product details

scrape_google_serp

Retrieves structured Google Search results

scrape_html

Automatically rotates proxies. Scrapes standard HTML from a URL

scrape_js_rendered

Scrapes a URL with JavaScript rendering enabled

scrape_premium

Scrapes a URL using high-quality residential proxies

Example Prompts for ScraperAPI in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with ScraperAPI immediately.

"Scrape an Amazon product page for this ASIN: B08J5F3G18 and list its price."

"Run a Google SERP check for the keyword 'best LLM orchestration frameworks'."

"Take a screenshot of https://netflix.com homepage so I can check its layout."

Troubleshooting ScraperAPI MCP Server with Pydantic AI

Common issues when connecting ScraperAPI to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

ScraperAPI + Pydantic AI FAQ

Common questions about integrating ScraperAPI MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your ScraperAPI MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.