How to Use the ZenRows MCP in Pydantic AI

Q: Can I validate multiple types of web scraping output with Pydantic AI?

Yes. You can use different scrapers—like scrapemarkdown or scrapegeo—and then run the resulting text through your agent's validation logic, ensuring all inputs are correctly typed.

Q: What is the best way to get structured data for Pydantic AI using ZenRows?

Use scrapeautoparse. It’s designed specifically to identify and structure information from web pages automatically, which minimizes post-processing errors.

Q: Do I need advanced options like premium proxies when using ZenRows with Pydantic AI?

It helps keep the data stream clean. scrapepremium gives you higher anonymity and reliability, ensuring that your agent's input isn't flagged or blocked midway through a critical run.

Validate every piece of web data using ZenRows with Pydantic AI.

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Connect ZenRows MCP to Pydantic AI

Create your Vinkius account to connect ZenRows to Pydantic 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 ZenRows with Pydantic AI

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Type-Safe Data Extraction for Pydantic Models

The `scrape_autoparse` tool extracts structured fields that map directly to your Python models. When the data comes back through the MCP Server, it's clean and predictable. This prevents silent corruption—the kind of junk data that makes an agent fail later.

Reliable Content Capture for Pydantic AI

Need to capture content from difficult sites? Use `scrape_antibot`. This tool ensures the input data is stable, so when your agent validates it against a Pydantic schema, it succeeds most of the time. It delivers reliable web inputs that maintain structural integrity.

Targeted Data Input for MCP Server

The `scrape_custom` tool lets you specify exact CSS selectors. This guarantees the agent only pulls data relevant to your defined Pydantic fields, ignoring noise. It’s surgical scraping; you get exactly what your model expects.

Setup guide

Set up ZenRows MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "zenrows-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to ZenRows tools.",
)

result = await agent.run("List recent ZenRows transactions")
print(result.output)

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Common questions about ZenRows MCP in Pydantic AI

ZenRows acts as the data source. It gathers raw, complex web content using tools like `scrape_html`, and then passes that structured input to your agent for validation against its schema.

Yes. You can use different scrapers—like `scrape_markdown` or `scrape_geo`—and then run the resulting text through your agent's validation logic, ensuring all inputs are correctly typed.

Use `scrape_autoparse`. It’s designed specifically to identify and structure information from web pages automatically, which minimizes post-processing errors.

It helps keep the data stream clean. `scrape_premium` gives you higher anonymity and reliability, ensuring that your agent's input isn't flagged or blocked midway through a critical run.

ZenRows touches public web content (HTML and structured text). The MCP Server handles the complexity of scraping so your agent only deals with validated, necessary inputs.

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