ZenRows MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect ZenRows through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 ZenRows "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in ZenRows?"
)
print(result.data)
asyncio.run(main())
* 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 ZenRows MCP Server
Connect your ZenRows account to any AI agent and harness the power of industrial-grade web scraping through natural conversation.
Pydantic AI validates every ZenRows tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through 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
- Universal Scraping — Retrieve raw HTML from any website while ZenRows automatically rotates proxies and handles CAPTCHAs
- JavaScript Rendering — Scrape dynamic SPAs and complex web apps by using a headless browser to capture the full rendered state
- Anti-Bot Bypass — Effortlessly bypass sophisticated protections like Cloudflare, DataDome, and PerimeterX with specialized bypass technology
- Markdown Conversion — Automatically convert web pages into clean Markdown, ideal for LLM ingestion and RAG applications
- Structured Data — Use auto-parse to extract JSON data from major e-commerce, search, and social platforms without manual selectors
- Visual Previews — Generate real-time screenshots of target pages to verify rendering or monitor visual changes
- Geographic Targeting — Execute scrapes using high-anonymity residential proxies from specific countries for localized content
The ZenRows 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 ZenRows to Pydantic AI via MCP
Follow these steps to integrate the ZenRows 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 ZenRows with type-safe schemas
Why Use Pydantic AI with the ZenRows MCP Server
Pydantic AI provides unique advantages when paired with ZenRows 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 ZenRows integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your ZenRows connection logic from agent behavior for testable, maintainable code
ZenRows + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the ZenRows MCP Server delivers measurable value.
Type-safe data pipelines: query ZenRows with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple ZenRows tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query ZenRows and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock ZenRows responses and write comprehensive agent tests
ZenRows MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect ZenRows to Pydantic AI via MCP:
get_screenshot
Generates a URL that returns a screenshot of the target page
scrape_antibot
Enables js_render and antibot=true. Scrape with full anti-bot bypass for heavily protected sites
scrape_autoparse
Scrape with automatic structured data extraction
scrape_custom
g. wait, css_extractor, session_id). Execute a scrape using advanced custom parameters
scrape_geo
g. "us", "gb") for localized content. Scrape using a proxy from a specific country
scrape_html
ZenRows automatically rotates proxies and handles CAPTCHAs. Scrape raw HTML using ZenRows anti-bot proxy pool
scrape_js
Enables js_render=true. Slower and more expensive than static scraping. Scrape JS-rendered HTML using ZenRows headless browser
scrape_markdown
Automatically removes boilerplate like navigation and ads. Scrape and convert page content to clean Markdown
scrape_premium
Sets premium_proxy=true for higher anonymity. Scrape using ZenRows premium residential proxies
scrape_wait
g. "#results") to wait for before capturing the HTML. Scrape with JS render waiting for a specific CSS selector
Example Prompts for ZenRows in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with ZenRows immediately.
"Scrape 'https://example.com' and return the content in Markdown."
"Bypass Cloudflare and scrape the rendered HTML of 'https://protected-site.com'."
"Get a screenshot of 'https://news-portal.com/breaking-news'."
Troubleshooting ZenRows MCP Server with Pydantic AI
Common issues when connecting ZenRows to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiZenRows + Pydantic AI FAQ
Common questions about integrating ZenRows MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
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?
Can I switch LLM providers without changing MCP code?
Connect ZenRows with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect ZenRows to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.